121 results about "Noise map" patented technology

The invention discloses a noise monitoring point site selection method and system based on a noise map, and relates to the technical field of environmental noise monitoring, and the method comprises the steps: constructing a three-dimensional topology network based on a three-dimensional noise intensity matrix, calculating an optimal sound propagation path through employing a quantum heuristic topology optimization algorithm, and obtaining an optimal sound propagation path; the weights of nodes in the three-dimensional topology network are optimized in combination with quantum annealing, and an acoustic topology characteristic spectrum is generated; extracting multi-scale noise propagation features through a convolutional neural network according to the acoustic topological feature map, constructing a gridding space demand matrix in combination with a monitoring equipment parameter library and coverage constraint conditions, and generating an optimal noise monitoring network site selection scheme according to the gridding space demand matrix; according to the invention, a quantum heuristic topology optimization algorithm is combined with quantum annealing to optimize the node weight of the three-dimensional topology network, and intelligent optimization and dynamic adjustment of the sound propagation path are realized.

The invention relates to the technical field of noise maps, in particular to a noise map dynamic deduction method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: carrying out the space-time alignment of a multi-cluster low-dimension feature vector in an edge calculation node, and obtaining a multi-cluster low-dimension feature vector; generating a three-dimensional sound field reconstruction parameter of the first target data sequence and a spectrum energy distribution parameter of the second target data sequence; based on the three-dimensional sound field reconstruction parameter and the spectrum energy distribution parameter, dynamically controlling the sensor cluster to sleep and generating a space-time difference parameter; hierarchical encryption is carried out on the time-space difference parameters and the two types of data sequences in the dormancy period, and a basic ciphertext sequence and a feature ciphertext sequence for verification are generated; according to the method, the noise deduction map with space-time continuity is generated, high-precision dynamic deduction and energy efficiency optimization of the noise map are achieved, the evolution rule of noise along with time and space is effectively reflected, the real-time performance and accuracy of noise data are improved, and the actual situation of the noise can be reflected more truly, efficiently and in real time.

The invention relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

The invention discloses an urban sound environment comprehensive treatment system based on the Internet of Things, which comprises a noise data monitoring and acquisition module, a noise data calculation and analysis module and a noise grading treatment module, and discloses an urban sound environment comprehensive treatment method. Real-time noise data monitoring and acquisition are carried out on specified urban areas, road sections and monitoring environments, and planning and layout can be carried out in the whole city; transmitting and preprocessing the real-time spectrum data by adopting a digital signal; determining the source of noise pollution on an established noise map by using a spatial positioning method according to the collected noise data through a set limit value; the method comprises the following steps: establishing a noise propagation mathematical model, analyzing a spatial relationship between noise data and a receptor, calculating and predicting noise levels of different areas, and comparing the noise levels with measured values; and different noise distributions are subjected to different-degree grading treatment according to the predicted value and the constructed noise map. According to the method, the performance of real-time noise monitoring and treatment feedback is improved.

The invention relates to the technical field of power systems, in particular to a transformer substation low-frequency noise source positioning method. The method comprises the following steps: obtaining a three-dimensional environment model of each structure in a transformer substation, and obtaining a geometric body topological relation matrix in the three-dimensional environment model; performing field measurement on the transformer substation to obtain noise measurement data; and performing outer convolution fusion on noise images between adjacent units according to the geometric topological relation matrix and the local noise image data, and iteratively spreading the noise images to the whole transformer substation area to generate initial sound source distribution estimation data of the whole transformer substation. The sound energy flow is utilized to analyze and identify the source position, the static source and the moving source are separated and tracked in combination with direction estimation and trajectory reconstruction, the static source can be accurately positioned, meanwhile, the motion trajectory of the moving source is obtained, and the separation efficiency is improved.

The invention relates to a sonar image generation method and device based on a diffusion model, equipment and a medium, and the method comprises the steps: carrying out the multi-time noise addition of each sonar actual measurement image through a forward process based on the diffusion model, and obtaining a plurality of noise images with different noise degrees; a plurality of noise images generated by the same image and corresponding semantic guidance graphs are used as a group of training data, the training data are input into a sonar image denoising network constructed based on a reverse process of a diffusion model to train the sonar image denoising network, in the training process, under the guidance of the semantic guidance graphs, the noise images are denoised for multiple times, and the noise images are denoised for multiple times. Learning parameters in the sonar image denoising network are adjusted according to a denoising result predicted by the network and an error of a corresponding real noise image, and finally, randomly generated noise images and semantic images are input into the trained sonar image denoising network for multiple times of sampling. And generating a sonar image related to the semantic image. By adopting the method, the high-quality sonar image can be efficiently generated.

The invention discloses a noise exposure evaluation method and system considering an unmanned aerial vehicle, and the method comprises the steps: obtaining urban environment data and unmanned aerial vehicle related data, carrying out the sound source modeling of the noise of the unmanned aerial vehicle, simulating the propagation of the noise of the unmanned aerial vehicle in an urban environment, and generating an unmanned aerial vehicle noise map; combining and calculating the unmanned aerial vehicle noise map and the current city noise map in combination with the current city noise map to obtain a city noise map; identifying crowd distribution conditions in different time periods and mapping the crowd distribution conditions to geographic space to form crowd distribution maps in different time periods, and performing feature identification and analysis on crowds in combination with multi-source data to obtain a crowd distribution map with crowd feature information; and according to the urban noise map and the crowd distribution map, calculating the total amount of regional noise pollution and the per capita noise pollution index, and calculating the noise exposure levels of different crowds. According to the method, the noise exposure evaluation method is perfected, and the space-time accuracy of unmanned aerial vehicle noise exposure evaluation is improved.

The invention discloses a VSP wellbore wave suppression method for improving U-Net based on physical constraints, which comprises the following steps: introducing apparent velocity constraints of an F-K transform domain, constructing physical constraint conditions, simultaneously realizing time sequence feature extraction and prediction of wellbore waves in combination with a GRU network, and learning a mapping relation between a noise image and a clean image by using the network so as to improve the suppression efficiency of the wellbore waves. The details and the structure of a noise image are accurately restored through a training network, the difference between a real de-noised image and network output is minimized so as to achieve the purpose of image de-noising, the model retains effective signals through a residual learning mechanism, and meanwhile precise suppression of shaft wave noise is achieved. The method provided by the invention can accurately identify and effectively suppress wellbore waves and reflected waves thereof in a complex VSP wave field, significantly improve the signal-to-noise ratio, reduce residual noise, improve data processing efficiency while improving data quality, provide technical support for high-precision seismic imaging under a complex geological condition, and have a wide application prospect. And meanwhile, a feasible solution is provided for popularization and application in an actual work area.

The invention provides a multi-modal image generation method and device. The method comprises the following steps: inputting a reference image and text description into a multi-modal image generation model; extracting image features of the reference image through an image feature extraction network; extracting text features of the text description through a text encoder; randomly generating a noise map through a noise map generation network; processing the noise map through an attention layer to obtain noise features; processing the text features and the noise features through a first cross attention layer to obtain attention features; and processing the image features and the attention features through the second cross attention layer to obtain a target image. By adopting the technical means, the problem that in the prior art, the generated image cannot meet the personalized effect of text description on the basis of keeping the key features of the reference image is solved.

The present invention relates to the technical field of three-dimensional noise map drawing, and specifically discloses a three-dimensional noise map calibration and drawing method based on vertical measured data, including: S1, obtaining multi-dimensional noise data and noise perception related data in the region, S2, constructing a preliminary three-dimensional noise distribution model, S3, drawing a noise perception contour map, S4, comparing the drawn noise contour map with the measured data and the perception data, identifying errors and S5, generating a risk map, and outputting a three-dimensional noise map; the present invention utilizes a geographic information system and three-dimensional visualization technology, combined with vertical measured data, to construct a preliminary three-dimensional noise distribution model, which can intuitively display the distribution of noise in three-dimensional space. Through a multimodal data fusion algorithm, the spatiotemporal weights of vertical monitoring data are introduced into the model, realizing the comprehensive embodiment of the physical noise level and spatial distribution impact, making the noise map closer to people's reality.

Embodiments described herein are generally directed to an end-to-end trainable degradation restoration network (DRN) that enhances the ability of a super-resolution (SR) subnetwork to deal with noisy low-resolution images. An embodiment of a method includes estimating, by a noise estimator (NE) subnetwork of the DRN, an estimated noise map for a noisy input image; and predicting, by the SR subnetwork of the DRN, a clean upscaled image based on the input image and the noise map by, for each of multiple conditional residual dense blocks (CRDBs) stacked within one or more cascade blocks representing the SR subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a multiple residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the input image by applying (i) a scaling factor and (ii) an offset factor derived from the noise map.

The invention discloses a remote sensing image super-resolution reconstruction method and device, a storage medium and equipment, relates to the technical field of computer vision, and can solve the technical problem that the remote sensing image super-resolution reconstruction effect is poor. The method comprises the following steps: acquiring a sample remote sensing image and multi-modal auxiliary data, extracting spectral features, textural features and geometric features of the pre-processed multi-modal auxiliary data, and fusing to generate a condition feature tensor; performing forward diffusion on the sample remote sensing image to generate a noise-containing image sequence; in the process of training the UNet diffusion network by using the noisy image sequence, injecting the conditional feature tensor into preset levels of an encoder and a decoder of the UNet diffusion network, and optimizing network parameters through multi-objective loss to obtain a trained UNet diffusion network; and obtaining a to-be-reconstructed noise map, performing noise prediction on the noise map based on the trained UNet diffusion network to obtain target noise, performing reverse sampling on the noise map, and generating a target reconstructed image through iterative denoising.

The invention relates to the technical field of data processing, in particular to an optimization method and device for an image generation model, electronic equipment and a storage medium, and the method comprises the steps: employing each training sample, taking a noise prediction result of a reference model as reference information, carrying out the multi-round iteration training of a basic image generation model, and obtaining a target model; in a round of iterative training, executing the following operations: based on the read training sample, obtaining a target noise reduction time step, a predicted first noise map and a predicted second noise map of the image generation model in random sampling, and obtaining a predicted third noise map and a predicted fourth noise map of the reference model in the target noise reduction time step; and adjusting the image generation model based on the difference between the object areas in the first noise map and the third noise map and the difference between the object areas in the second noise map and the fourth noise map. Therefore, the image generation effect of the object area can be effectively improved.

The invention provides a marine acoustic environment monitoring system based on a noise map. The marine acoustic environment monitoring system comprises a buoy network module, a data fusion processing module, a noise map generation module, a noise source identification module and an edge response module, the buoy network module serves as a sensing tail end, multi-source information is gathered to the data fusion processing module through the first data bus for standardization processing, and then the noise map generation module is driven through the second data bus to construct a three-dimensional sound field model. The output of the model is transmitted to a noise source identification module through a third data bus for intelligent analysis and traceability; the alarm decision of the noise source identification module is output to the edge response module, and the edge response module dynamically regulates and controls the communication and acquisition strategy of each buoy node through a distributed control circuit to form a complete closed loop. According to the invention, high-precision and automatic monitoring of the marine acoustic environment is realized, the problems of limited monitoring range and single data dimension of the traditional means are solved, and the reliability and emergency capability of the system are improved.

The invention relates to a multi-source data fusion-based Ningxia cell noise map construction and evaluation method, which comprises the following steps of: establishing a frequency-variable sound absorption parameter matrix of a building surface based on multi-spectral scanning data and an acoustic database, and combining a burst noise voiceprint recognition result and traffic monitoring data, generating a burst noise influence map through the burst noise space-time correlation model; the real-time sound pressure data and the frequency-variable sound absorption parameter matrix are fused, and a sound field is dynamically reconstructed to generate a noise thermodynamic diagram; and based on a Zwicker loudness model, coupling a burst noise influence map and resident electrocardiogram monitoring data, calculating a composite noise dysphoria index, constructing a noise map in combination with a noise thermodynamic diagram, and carrying out comprehensive evaluation by adopting an entropy weight-TOPSIS algorithm to obtain a noise evaluation result. According to the method, the noise map is constructed through multi-source heterogeneous data fusion, dynamic sound field modeling and physiological and psychological coupling calculation, and evaluation is performed in combination with multi-dimensional indexes, so that precise evaluation of a cell noise environment and scientific dynamic optimization of a noise reduction scheme are realized, and objectivity and pertinence of noise treatment are effectively improved.

The invention discloses a data processing method and device, electronic equipment and a readable storage medium, and the method comprises the steps: generating a target noise map of an initial map through employing a noise map generation function based on a first UV coordinate of the initial map; processing the target noise map by using a first periodic function to obtain first offset information; using the first offset information to disturb the first UV coordinate of the initial map to obtain a second UV coordinate of the initial map; sampling the target noise map based on the second UV coordinate to obtain a first height map; and generating a normal map based on the first height map. A normal map including height information is generated through the method so as to restore the flow effect of the material with the flow characteristic in the real world.

Aspects of the methods, apparatus, non-transitory computer readable medium, and systems include obtaining a noise map and a global image code encoded from an original image and representing semantic content of the original image; generating a plurality of image patches based on the noise map and the global image code using a diffusion model; and combining the plurality of image patches to produce an output image including the semantic content.

The invention discloses a city noise map generation method, system and device and a storage medium, and relates to the technical field of environment monitoring, and the method comprises the steps: collecting the noise data of each region of a city through a fixed sensor and a mobile sensor, extracting the spatial-temporal characteristics related to the noise intensity in the noise data, and carrying out the integration, the method comprises the following steps: acquiring noise intensity and noise source positions of different types of noise sources, performing noise level classification according to the noise intensity, acquiring noise levels, integrating the noise source positions, the noise levels and acquisition time of the different types of noise sources, placing an integration result in a map, and generating a noise distribution map with different noise levels. According to the method, the change of noise pollution and the region with high noise level are found and dealt with, the noise distribution condition of each region of a city is accurately described through data integration of the data, and a scientific basis is provided for city planning, environmental protection and public health management.

The invention relates to an image forgery positioning detection method based on collaborative difference optimization and multi-modal perception, and belongs to the technical field of computer vision and multimedia evidence obtaining. The method comprises the following steps: obtaining an RGB image and carrying out random data enhancement; extracting a noise map by using a noise extractor; the RGB image and the noise graph generate multi-scale features through a pre-trained cross-modal information encoder; the multi-scale features are input into an exception encoder, the exception encoder comprises a multi-layer perceptron, a linear fusion block and a linear prediction block, multi-layer mapping features are obtained through processing of the multi-layer perceptron, and an initial prediction map is output; inputting the original RGB image into a ViT model to extract global features, and inputting the global features and the multilayer mapping features into a cross-scale feature enhancement module for interactive optimization to obtain a refined positioning map; fusing the initial prediction map and the refined positioning map to obtain a final prediction map; and iterative optimization is carried out through the loss function. According to the invention, the detection sensitivity of tampered areas with different sizes can be improved.

The invention discloses a low-illumination underwater image enhancement method based on Zero-shot learning and improved white balance, and the method comprises the following steps: S1, inputting an image into a three-branch full convolutional network, decomposing the image into a reflection image, an illumination image and a noise image through the three-branch full convolutional network, then constructing a loss function composed of reconstruction loss, texture detail loss and noise loss, carrying out iterative fusion on the reflection image by using the loss function, carrying out noise prediction and denoising by using a noise image in the iteration process, and carrying out illumination recovery and iterative convergence on the reflection image by using an illumination image to obtain a fused image; s2, carrying out secondary iterative calculation on the fused image through a gray world algorithm; the image is processed through the three-branch full convolutional network, data set training is not needed, then white balance processing is carried out on the processed image through the secondary gray world algorithm and the perfect reflection algorithm, the problems of color cast, chromatic aberration and contrast of the image are reduced, and meanwhile the method has the advantage of being high in processing efficiency.

The invention discloses a sand storm prediction method based on multi-source data fusion and conditional diffusion generation, and the method comprises the steps: definitely defining a sand storm prediction task as a time-space sequence prediction problem, inputting meteorological data at a plurality of moments, and outputting sand storm change information at a plurality of moments; performing noise addition on the sand storm data according to a noise addition formula to obtain sand storm data with noise; jointly training a multi-source data noise prediction network through a satellite cloud picture at a historical moment, meteorological reanalysis data, sand storm data and a time step t; inputting the complete Gaussian noise map, the current time step tx, and the satellite cloud map, the meteorological reanalysis data and the sand storm data at the historical moment into a multi-source data noise prediction network, predicting the noise of the current noise map, and removing the noise at the current moment through a denoising formula to obtain the sand storm data with noise at the tx-1 moment; and repeating the operation until the t0 moment to obtain a prediction result. According to the invention, sand storm prediction can be accurately carried out.

The present disclosure relates to the technical field of computers, and specifically relates to a virtual object rendering method, a virtual object rendering apparatus, a storage medium, and a device. The method comprises: acquiring a first coordinate variable and a second coordinate variable, and acquiring a first noise map and a second noise map; sampling the first noise map by means of the first coordinate variable to obtain a first sampling result, and sampling the second noise map by means of the second coordinate variable to obtain a second sampling result; performing interpolation on the first sampling result and the second sampling result to obtain a third sampling result; acquiring a texture map corresponding to a virtual object; and superposing the third sampling result on a map coordinate to obtain a target map coordinate, and sampling the texture map by means of the target map coordinate so as to render the floating effect of the virtual object in a two-dimensional space. According to the technical solution of the embodiments of the present disclosure, the problem in the prior art of poor efficiency of realizing the floating effect in a two-dimensional space can be solved.

The invention discloses an image generation method, a model training method and a related device, and the method comprises the steps: extracting the input information to obtain a style feature, and enabling the style feature to be used for representing the noise level of an image; generating a target noise map corresponding to the to-be-processed image based on the style features; and fusing the target noise map with the to-be-processed image to obtain the target image consistent with the expected image noise level, so that the problem of noise enhancement and weakening of the to-be-processed image can be solved.

The present invention discloses a method, system and storage medium for dynamically updating a large-scale noise map, belonging to the technical field of noise monitoring. The method includes: S1: According to the monitoring ranges covered by different central stations, obtain corresponding geographic model data and send it to the corresponding central stations; S2: Each central station obtains the noise data of the microsites that have established connections; S3: Generate noise map data; S4: Each central station sends the noise map data to the server through the network; S5: The server automatically publishes the noise map data to the spatial geographic information sharing software; S6: The front end sends a request to the server to obtain the noise map data of all central stations at the target moment and perform visual display. A monitoring network established by 1 central station and n microsites is deployed in each area to realize the real-time collection of noise data and large-scale coverage of different functional area ranges, ensuring the timeliness, accuracy and diversity of noise data.

Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.

The invention discloses a colored shaded relief map generation method and system fusing land coverage information. The method comprises the steps of obtaining DEM data and land coverage data of a target area, taking the preprocessed DEM data as DEM condition information, and taking the preprocessed land coverage data as land coverage condition information; the method comprises the following steps: inputting a randomly generated noise image and DEM condition information into a backbone network, inputting the noise image, the DEM condition information and land coverage condition information into a ControlNet fine tuning module to obtain a correction signal, inputting the correction signal into the backbone network to obtain predicted noise, and performing reverse denoising based on the predicted noise to obtain a colored shaded relief map. According to the method, the semantic correctness of the generated color can be ensured, and the generated colored shaded relief image better conforms to the visual effect of a real natural landscape.

The invention provides a virtual scene rendering method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: respectively obtaining a preset model and a preset noise map; mapping each unit patch in the preset model to each pixel point in the preset noise map, and obtaining a color channel parameter corresponding to each unit patch; according to the color channel parameters corresponding to the unit patches, diffusion parameters corresponding to the unit patches are determined; and according to the diffusion parameter corresponding to each unit patch and the preset texture map corresponding to the preset virtual overlay, rendering to generate an animation rendering effect that the preset virtual overlay falls along the preset direction in the virtual scene, by applying the method and the device, spatial distribution and movement of the unit patches can be realized based on the preset noise map, and the animation rendering effect is improved. Therefore, the animation rendering effect that the preset virtual overlay falls in the preset direction in the virtual scene is rendered and generated, the method has the advantage of being simple in calculation, and the operation performance can be improved.

The disclosure describes a training method for training a system for de-noising images, which comprises an input-interface and a number of trainable bilateral filters designed and arranged for filtering an image provided by the input interface. The training method includes providing a plurality of training images as input for the system, providing a number of noise maps indicating the standard deviation of the noise for every pixel of a training image, training the number of bilateral filters being based on the training images and the number of noise maps, and calculating analytical gradients of a loss function with respect to filter parameters of the system. At least one of the loss functions is based on Stein's unbiased risk estimator.

The embodiment of the invention relates to an image processing method and device, equipment and a medium. The method comprises the steps that a first image and a first noise map are acquired; wherein the first image comprises a target object, and the transparency of the target object in the first image is different from the transparency of an area outside the edge of the target object; performing fuzzy processing on the first image to obtain a fuzzy image corresponding to the first image; generating a second image corresponding to the first image based on the transparency information of the blurred image and the first noise map; wherein the edge of the target object in the second image is obtained by acting the first noise map on the edge of the target object in the first image based on the transparency information of the blurred image. According to the invention, special effect processing on the object edge in the image can be efficiently and conveniently realized, and the realization efficiency of the burr special effect is effectively improved.

The present invention is related to a Raspberry Pi enabled smart system for monitoring excessive sound levels in decibels, provides an intelligent, real-time solution for monitoring, analyzing, and controlling noise in various environments. The system integrates a Raspberry Pi microcontroller with both analog and digital microphones to accurately measure sound intensity, while a relay module enables automatic control of sound sources when predefined thresholds are exceeded. Immediate local alerts are provided through buzzers, LEDs, and an OLED display, while real-time data is transmitted to cloud-based IoT platforms for remote monitoring, visualization, and historical analysis. A dual-microphone setup allows automatic calibration to improve measurement accuracy, and the system computes cumulative sound exposure for compliance tracking. Scalable deployment enables multiple units to form a distributed wireless sensor network for large-area noise mapping with potential GPS-based localization. This integrated approach combines real-time sensing, automated control, cloud monitoring, and data analytics, offering a comprehensive solution for industrial, educational, and public safety applications.