877 results about "Noise level" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

A system and method for real-time, identity-free personalization using deformable emotional trait vectors to dynamically adapt digital and voice-based experiences. Each user session is modeled as a behavioral object known as a Vectra, composed of fluidic traits—such as mass, viscosity, temperature, volatility, and texture—that evolve continuously in response to live behavioral, contextual, environmental, and voice-derived signals. These Vectras traverse a dynamically warped emotional space, the Vectraverse, influenced by ambient conditions including time of day, noise level, inventory urgency, and engagement rhythm. Gravitational pull toward predefined emotional goal attractors modulates system behavior, while a goal mutation engine reclassifies session intent when confidence decays or friction spikes. Outputs include tone modulation, content pacing, offer framing, and gamified reward logic—all executed without storing identity, login credentials, or historical profiles. The system supports modular deployment across voice, screen, signage, mobile, and in-room environments, and integrates with large language models, AI agents, and third-party personalization stacks via privacy-safe APIs and federated learning. Designed for zero-ID personalization, the platform enables emotionally intelligent, context-aware engagement across any surface or session.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention discloses a pattern recognition type metal pipeline anticorrosive coating quality detection device and system, belongs to the technical field of sensing monitoring, and aims to solve the problems of low detection precision and difficulty in positioning of damaged points of a metal pipeline anticorrosive coating under the interference of a complex environment. The method comprises the following steps: generating an environmental interference spectrum feature library through full-range frequency band pre-scanning, dynamically establishing a reference noise level, screening candidate frequencies of detection signals, and switching the frequencies as required to guarantee signal quality; synchronously acquiring a potential gradient and an electromagnetic radiation signal, and marking space-time coordinates; performing deep processing on the acquired signals, separating multi-frequency signals, correcting attenuation, and constructing a multi-dimensional feature vector; based on the feature vector, a space-time abnormal mode is extracted to position a damage point, through damage type classification, region area measurement and depth evaluation, the damage severity is quantified, accurate data support is provided for pipeline maintenance, and the accuracy and reliability of metal pipeline anticorrosive coating detection are effectively improved.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention is suitable for the technical field of inverter detection, and provides an arc discharge detection method and device of an inverter, electronic equipment and a storage medium. The method comprises the following steps: inputting working current data of a photovoltaic inverter into an initial arc discharge detection model; in the preset continuous duration of the first operation, if the output result is normal operation and the confidence value is greater than the threshold value, taking the corresponding working current data as a sample to form a sample data set; performing online training on the initial arc discharge detection model by using the sample data set to obtain a candidate arc discharge detection model; under the condition that the candidate arc discharge detection model meets a preset requirement, determining the candidate arc discharge detection model as a target arc discharge detection model; and finally, performing arc discharge detection on the real-time current window based on the target arc discharge detection model in online operation. According to the scheme, on-line training is carried out on site, so that model parameters can be self-adapted in real time along with fluctuation of a power grid on site, load disturbance and noise level, and the arc discharge detection precision is improved.

The invention discloses a non-convex contiguous group sparse variational filtering hyper-parameter adaptive calculation method, which comprises the following steps of: selecting a reference signal according to ultrasonic signal characteristics, and determining the size of a group sparse group through a specific formula in combination with a sampling frequency and a probe frequency; after interference removal is carried out on the ultrasonic signal, a scattering noise standard deviation is calculated based on the reference signal, and a regularization parameter is obtained in combination with a formula; calculating a penalty factor; when the detection sound path distance is less than 1000mm, adopting global filtering; when the sound path distance is detected to be larger than 1000 mm, local filtering is adopted, a punishment intensity reference value is estimated based on the noise level, the reference value is corrected in combination with the signal-to-noise ratio, and then a formula is substituted to calculate a punishment factor coefficient and a final punishment factor; in the iteration process, the relative variable quantity of the cost function is used as a convergence criterion, iteration is stopped when the variable quantity is smaller than a preset threshold value, and efficient and stable filtering is ensured; the method completely meets the engineering application requirements of industrial ultrasonic detection.

The invention discloses a short-process low-cost preparation method for forging a TC4 titanium alloy bar. The short-process low-cost preparation method comprises the following steps: (1) cogging and forging a beta-phase region; (2) performing drawing-out thermal deformation on an alpha + beta region; (3) static quasi-beta annealing water quenching treatment; (4) carrying out thermal deformation in a near alpha + beta region; and (5) alpha + beta area small-deformation multi-tempering drawing-out finish forging forming is carried out. The method has the advantages that the requirements of uniform orientation distribution, sufficient grain refinement, process simplification and convenient operation in the short-process low-cost TC4 titanium alloy manufacturing bar are met, the structure uniformity of the large bar is improved, the obtaining of a homogeneous fine structure is facilitated, the requirement of nondestructive testing of the flaw detection clutter level is met, and the product quality is improved.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.

The invention discloses a dynamic adaptive black level correction method and device, and relates to the technical field of image processing. According to the dark current space distribution difference, an effective pixel area in each frame of original image is dynamically divided; determining an initial black level calibration coefficient of each space block based on the dark pixel noise level; obtaining the current temperature and the temperature change rate of each space block, and obtaining the level calibration coefficient of each space block after temperature self-adaptive calibration based on the initial black level calibration coefficient corresponding to each space block; fusing the calibration information of the previous frame and the level calibration coefficient of the current frame to obtain a cross-frame stable calibration result; and correcting the effective pixels in each space block based on a cross-frame stable calibration result to obtain a corrected pixel value, and reconstructing the corrected pixel value into an output image. The problems that an existing black level calibration technology is poor in space uniformity, insufficient in temperature change adaptability and unstable in calibration coefficient are solved.

The invention discloses an intelligent noise reduction method and system based on a mine earphone, and the method comprises the following steps: S1, obtaining an original noise signal in a mine operation environment, and carrying out the preprocessing of the original noise signal; s2, designing an adaptive compensation mechanism according to the reflection characteristics of the closed space of the mine, calculating a sound wave propagation attenuation coefficient and reflection delay, constructing a roadway reverberation model, and generating advanced compensation sound waves; and S3, carrying out collaborative fusion on the advanced compensation sound wave and a main noise reduction signal to generate a composite noise reduction signal. The invention relates to the technical field of intelligent noise reduction. According to the intelligent noise reduction method and system based on the mine earphone, the noise level in a mine operation environment is effectively reduced, the communication efficiency and the working comfort of operators are improved, potential damage of noise to hearing of the operators is reduced, and therefore the overall safety and the production efficiency of mine operation are improved.

The invention belongs to the field of man-machine interaction, and particularly discloses a man-machine interaction method for electronic screen control, which comprises the following steps: acquiring touch track data of a user according to a touch sensor, and performing feature extraction on the touch track data to obtain a touch feature vector; the method comprises the following steps: acquiring gesture action data of a user according to image acquisition equipment, and performing key point detection on the gesture action data to obtain a gesture feature vector; the method comprises the following steps: acquiring voice instruction data of a user according to an audio acquisition device, and performing acoustic feature extraction on the voice instruction data to obtain a voice feature vector; acquiring current environment parameters in real time through an environment detection module, wherein the environment parameters comprise illumination intensity, environment noise level and distance between a user and a screen; the objective of the invention is to solve the problem of insufficient reliability of a man-machine interaction mode in a complex environment in the prior art.

The utility model relates to the field of fuel gas pressure regulation, in particular to a double-layer noise reduction valve cage and a regulating valve, which comprises an inner-layer valve cage, a plurality of first noise reduction holes, a second-layer valve cage and a third-layer valve cage, the outer-layer valve cage is arranged outside the inner-layer valve cage in a sleeving manner and fixedly connected with the inner-layer valve cage, and a plurality of second silencing holes which are arranged according to a second rule are formed in the outer-layer valve cage; and the inner-layer valve cage and the outer-layer valve cage are coaxially arranged. According to the utility model, the inner-layer valve cage is used for preliminarily dispersing pressure waves and noise of fluid through the plurality of first silencing holes arranged according to the first rule, the outer-layer valve cage is used for further absorbing and scattering residual noise generated by the inner-layer fluid through the second silencing holes, and the inner-layer valve cage and the outer-layer valve cage are coaxially arranged; the additional flow resistance generated when fluid penetrates through different layers can be reduced, and meanwhile the continuity of a flow channel is guaranteed. Under the conditions of high pressure difference and large flow, noise of the regulating valve is effectively reduced, it is ensured that the noise level does not exceed 85 decibels, and the requirements of specific application occasions are met.

The invention discloses an automatic test method and device based on a camera and an electronic device, and the method comprises the steps: configuring a test environment which comprises an adjustable light source and a standard test graphic card; the brightness and color temperature of the adjustable light source and the focal length and exposure parameters of the to-be-detected camera are automatically adjusted through the control module; controlling the to-be-tested camera to acquire an image of the standard test graphic card, and transmitting the acquired image to an image processing module; the image is analyzed through an image processing module, and the resolution, the color rendition degree, the noise level and the distortion rate are calculated; and generating a test report according to an analysis result, and judging whether the to-be-tested camera is qualified or not. According to the scheme provided by the invention, the adjustable light is automatically controlled to test the camera, the cost of a test task is reduced, the test efficiency is improved, and in addition, the camera test precision is improved through various abundant test conditions.

The utility model discloses an indoor tire road noise testing device, which comprises a sound insulation box body, a slope table, a tire driving system and a noise acquisition system, the sound insulation box body is of a closed structure. The slope table is arranged in the sound insulation box body, the slope table is provided with a slope adjusting device, the slope adjusting device is used for adjusting the slope angle of the slope table, and the slope of the slope table is used for placing a pavement test piece; the tire driving system comprises a driving assembly and a tire fixer, the tire fixer is rotatably mounted at the output end of the driving assembly, the tire fixer is arranged in the sound insulation box body and is used for mounting a test tire, and the driving assembly is used for driving the tire fixer to move to drive the test tire to impact the pavement test piece; the noise acquisition system is used for acquiring sound data in the sound insulation box body. The slope table is used for simulating the actual road surface condition, the noise condition of the designed road surface can be tested in the design stage, the actual effect is effectively guaranteed, and the problem that low-noise road surface design is difficult to verify in the design stage is solved.

The invention relates to a multi-parameter estimation algorithm combining a disturbance quantification model and a rooting method in interference measurement. In order to solve the problems that in the prior art, the disturbance degree of a sequence is difficult to quantify, and harmonic key parameters are influenced by spectrum leakage, the invention provides the following innovative scheme: firstly, a sequence multi-classification measurement entropy quantification model is constructed, and quantitative evaluation of the signal noise degree is realized; secondly, through a Hankel matrix construction and rooting parameter estimation method, realizing accurate estimation of the harmonic frequency of the real-value signal; meanwhile, an amplitude correction algorithm based on window function characteristics is designed, and the signal sampling condition is effectively judged; and finally, through matrix weighting operation of a phase demodulation function, efficient reconstruction of an optical surface shape is realized, the problems of spectrum leakage and disturbance quantification in a traditional method are solved, and the method has relatively high measurement precision.

A device structure includes a first series connection of a first phase change memory (PCM) switch and a second PCM switch. The first PCM switch includes a first heater line, a first PCM line, and a first contact electrode and a second contact electrode located on the first heater line. The second PCM switch includes a second heater line, a second PCM line, and a third contact electrode and a fourth contact electrode located on the second heater line. The second contact electrode is electrically connected to the third contact electrode. The fourth contact electrode is electrically grounded. One of the first contact electrode and the second contact electrode includes an radio-frequency (RF) signal input port. Another of the first contact electrode and the second contact electrode comprises an RF signal output port. The device structure may function as a combination PCM switch that decreases noise level during signal transmission.

The invention discloses a composite board production quality management and control system, and relates to the technical field of composite board production. The multi-modal sensing fusion module is used for performing weighted fusion on detection data output by the geometric detection unit, the interlayer and defect detection unit and the process parameter detection unit, and dynamically adjusting weights of various data according to the production line speed, the environment temperature and the sensor noise level so as to improve the accuracy of defect identification and process abnormality judgment; and the core algorithm and control logic layer is used for analyzing and processing detection data in the production process of the composite board and outputting a control instruction. The multi-modal sensing fusion module is arranged to carry out centralized processing on data of various types of detection units, so that multi-source information is coordinated and cross-modal data fusion is carried out, the recognition precision of complex defects is improved, and the detection stability is ensured.

The invention discloses a medical image generation method based on cyclic diffusion and target guide sampling. According to the method, existing source modal medical images are converted into other target modal medical images through cyclic diffusion. An additional attention mechanism is introduced into a noise prediction network of a diffusion model of the cyclic diffusion model, and anatomical structure information in the source modal medical image is fused with a current noise image; meanwhile, the anatomical structure segmentation network introduces anatomical structure shape loss. When the diffusion model is trained, a perceptual priority weighted P2W training strategy is adopted: by adjusting the weight of a loss function, the diffusion model preferentially learns the global structure of the image at a high noise level and optimizes the details of the image at a low noise level; meanwhile, a target-guided classifier for a sampling stage is trained. In a sampling stage, first, reverse sampling is performed from a source modal image to obtain a potential code; and under the guidance of the classifier, the sampling process is guided by utilizing the probability gradient information of the target modal, and the generation of a target modal image is completed.

The invention relates to the technical field of power equipment anomaly detection, and provides a multi-modal fusion power equipment anomaly detection method and system, and the method comprises the steps: obtaining modal data after cross-modal calibration through a cross-modal calibration model for each piece of modal data; for each modal data after cross-modal calibration, a double-branch structure is adopted, time sequence features and spatial topology features are extracted respectively, and spatial-temporal joint features are obtained after splicing; on the basis of the spatio-temporal joint features, correlation between modals is calculated, the environmental parameters and the noise level of each modal data acquisition sensor are combined, the weight of each modal is calculated, weighted fusion is carried out on the spatio-temporal joint features of each modal, and fusion features are obtained; based on the fusion features, the state change trend of the power equipment is predicted through the multi-modal large model, the abnormal time point is identified, and the abnormal detection precision of the power equipment is improved.

The invention discloses an automobile noise monitoring system, and relates to the field of vehicles. Comprising a detection part arranged on the outer side of a vehicle and a processing display part arranged in the vehicle, and the detection part comprises a first shell fixed outside a vehicle body and a sensor module fixed to the outer wall of the first shell and used for wind speed detection; the processing display part comprises a second shell fixed in the vehicle through a fixing base, the outer side of the second shell is provided with a double-color lamp display type logo display area, a noise size display area and a loudspeaker box, a central processing main board is arranged in the second shell, and a back plate is fixed to the back of the second shell. The vehicle speed information and the noise information of the vehicle are collected in real time based on the sensor module, and then the abnormal information of the vehicle can be reported to the traffic administration bureau in time through the communication unit when the vehicle is abnormal by adopting a mode based on threshold control, so that full-coverage real-time supervision of the vehicle is realized.

The invention discloses a self-adaptive point cloud implicit curved surface reconstruction method based on a truncation level B spline, and the method comprises the steps: constructing a first-layer truncation level B spline primary function space, and obtaining an initial implicit function through solving an optimization model; evaluating the fitting error of each point of the input point cloud, and determining the fitting error as a region which needs to be further refined; establishing a new truncation hierarchy B-spline primary function set, and expanding a current function space; performing truncation operation on the THB spline primary function which is completely positioned in the refined region; combining the truncated primary function with the primary function set generated by the new layer, and updating the whole primary function library; re-optimizing the model, and solving the updated implicit function; refining and optimizing layer by layer, and outputting a final implicit function and a zero level set thereof; and performing triangular meshing on the final zero level set to finish curved surface reconstruction. According to the method, an extra training process is not needed, excellent point-to-surface distance and curvature indexes can be kept at the noise level up to 10%, and the memory and calculation overhead can be greatly reduced.

The invention provides a wind profile radar spectrum peak estimation method based on a three-parameter dynamic cost function, and the method comprises the steps: 1, carrying out the preprocessing of distance and Doppler spectrum data, and carrying out the three-point moving average smoothing; step 2, noise level estimation based on statistics is carried out; step 3, carrying out candidate spectrum peak selection based on space Doppler window smoothing; 4, performing dynamic weight calculation based on covariance matrix eigenvalue decomposition; step 5, constructing a three-parameter cost function of a distance spectrum peak value RSP power item, a continuity item and a spectrum width consistency item, and performing optimization; and step 6, outputting the optimal spectrum peak trajectory to form a complete wind speed profile trajectory. According to the method, the spectrum peak identification precision and stability in a complex environment can be improved, and the u and v wind correlation coefficients of the estimation method are verified to reach 0.880 and 0.879 through a data set disclosed by an atmospheric radiation measurement website.

The embodiment of the invention provides a liquid cooling energy storage system and a control method. The liquid cooling energy storage system comprises an energy storage module, a liquid supply pipeline, a liquid return pipeline and at least two groups of liquid cooling machines, each group of liquid cooling machines comprises at least one liquid cooling machine, liquid outlets of different groups of liquid cooling machines are communicated with the liquid supply pipeline in parallel, liquid return ports of different groups of liquid cooling machines are communicated with the liquid return pipeline in parallel, the liquid supply pipeline is communicated with a liquid inlet of the energy storage module, the liquid return pipeline is communicated with a liquid return port of the energy storage module, and the liquid cooling machines are used for providing cooling liquid. When the heating value of the energy storage module is small, only part of the liquid cooling units need to be started, and compared with a liquid cooling unit with large power in the prior art, the liquid cooling unit is low in energy consumption, high in efficiency and low in noise during operation. And when any one liquid cooling machine breaks down, other liquid cooling machines can still provide the required cooling capacity, the heat dissipation requirement of the energy storage module is met, and the maintenance flexibility is improved.

The invention discloses an outdoor space public facility layout optimization method based on big data analysis, and belongs to the field of outdoor space public facility layout optimization, and the method comprises the steps: predicting the number of participants in a data set based on demands, and combining the illumination intensity, wind direction, temperature and rainfall in a comprehensive data set, calculating a meteorological suitability score of each region, and generating a preliminary facility layout data set including candidate position points and meteorological suitability scores thereof; on the basis of the adjusted candidate position data set, in combination with the average decibel value of each region, spatial analysis is executed, position points with the average decibel value lower than 60 decibels are screened, and an optimized position data set is generated and comprises the position points, a meteorological suitability score and a noise level; and based on the optimized position data set, in combination with the facility coverage range and the construction and maintenance cost, calculating the facility number and type of each region, and generating a facility layout planning data set including a facility configuration list of each region.

The invention discloses an acoustic excitation digital modulation noise reduction method and system for laser acoustic vibration detection and application. Comprising the following steps: step 1, acquiring single-point measurement time sequence parameters of laser vibration measurement equipment, step 2, measuring response characteristics of an audio excitation device, and determining pre-stabilization time required by an audio excitation signal from starting to reaching a stable amplitude; step 3, constructing an on-off keying modulation function, generating a digital gating function according to the time sequence parameter and the pre-stabilization time, and performing time domain multiplication on the audio excitation signal and the digital gating function to generate a modulated intermittent audio excitation signal; and 4, controlling the audio excitation device to play intermittent audio excitation signals, and synchronously triggering the laser vibration measurement equipment to scan and collect the measurement points on the surface of the object to be measured according to the time sequence parameters. Through the on-off keying digital modulation technology, audio excitation can be started only in the effective detection time period of laser vibration measurement, and therefore the average noise level and equipment power consumption are remarkably reduced.