31 results about "Spatial quality" patented technology

The invention provides a highway engineering quality real-time monitoring method and system based on digital twinning, and the method comprises the steps: collecting construction data, and carrying out the preprocessing of the construction data, and obtaining a behavior time sequence and a coverage rate index sequence; constructing a behavior graph, performing quality prediction on each behavior segment node through a graph neural network to obtain a predicted quality score, and performing coverage rate index sequence correction to obtain an actual quality score; mapping the actual quality score to a spatial position to obtain a spatial quality tensor, wherein the spatial position is a three-dimensional position obtained by combining equipment position data and a compaction horizon number; performing structural consistency analysis on the spatial quality tensor, identifying and marking a topology abnormal region, and outputting a structural consistency scoring tensor; and generating a visual twin layer based on the spatial quality tensor and the structural consistency scoring tensor, and performing graph level labeling and section mapping on the abnormal region.

The invention relates to an urban land surface environment dynamic monitoring method and system fused with a remote sensing technology. The method comprises the following steps: acquiring an original data set of a target city; performing data cleaning and standardization processing on the original data set to generate a standardized urban space data set; segmenting a city and town development boundary according to the data set, dividing evaluation units and designing a multi-dimensional evaluation index system; on the basis of the evaluation index system, measuring and calculating each index and determining the weight, and obtaining a three-level evaluation grade for evaluating the spatial quality; and finally, on the basis of the three-level evaluation level, targeted quality improvement countermeasures are formulated according to the spatial quality characteristics of different areas. By adopting the method, the urban space quality difference can be accurately identified, a quantitative basis is provided for urban planning, resource allocation optimization, facility layout improvement, heat island effect alleviation and the like are assisted, refined treatment and sustainable development of urban space are promoted, and the life welfare of residents is improved.

The invention relates to the technical field of point cloud data processing, in particular to a point cloud arbitrary multiplying power up-sampling method and system under spatial geometric distribution constraint. The method comprises the steps of performing pre-interpolation on acquired laser radar point cloud data, calculating a local score according to spatial geometric distribution around each point in the interpolated point cloud, and further screening out abnormal interpolation points of which the scores are lower than a threshold value to obtain initial upper sampling point cloud data; extracting local and global features of the data; calculating an error value between each point and a real point cloud through a point-to-point distance regression network based on the data and the features; and iteratively optimizing the point coordinates by taking the minimum error value as a target to obtain optimized upper sampling point cloud data. The method effectively solves the problems of aggregation, non-uniform sparseness, noise points and the like of the collected original point clouds, improves the spatial quality and geometric accuracy of the point cloud data, can achieve the up-sampling of the point clouds at any multiplying power, and is suitable for different application scenes.

The invention provides an audio quality determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first sound recording signal obtained by recording preset first audio data played by a to-be-tested audio playing device, and obtaining a first sound recording signal compared with a mode of obtaining the first sound recording signal through synthesis; the accuracy of obtaining the first recording signal is improved, then a tone reference signal and a spatial reference signal corresponding to the to-be-tested audio playing device are obtained, and a first tone feature and a first spatial feature corresponding to the first recording signal are determined according to the tone reference signal, the spatial reference signal and the first recording signal; the first timbre feature and the first spatial feature are input into the perception model obtained through training, the first timbre quality score and the first spatial quality score of the to-be-tested audio playing device are obtained, the reference signal is not fixed any more, and the accuracy of subsequent multi-dimensional quality scoring is improved.

The application relates to a multispectral and panchromatic image fusion spatial quality evaluation method based on a region convolution network, which improves the processing process of a fused image and a panchromatic image in a traditional region-based multispectral image and panchromatic image fusion spatial quality evaluation method. The spatial features of region images in the original fused image and the panchromatic image are directly extracted by using a GoogLeNet network, instead of performing gray image conversion and discrete cosine transformation on the fused image. The spatial information loss of the image is effectively avoided, and meanwhile, the deep features with stronger representation ability and generalization ability are extracted, so that the spatial quality of a fusion result is evaluated at a feature level. Finally, an intuitive spatial quality distribution map is obtained to represent the spatial quality of local regions of the fusion result.

The invention relates to a satellite hyperspectral remote sensing image generation comprehensive quality evaluation system and method, and the method comprises the steps: constructing a comprehensive quality evaluation system which comprises a spectrum quality evaluation index, a space quality evaluation index and a texture quality evaluation index; and calculating the importance of overall evaluation results of spectrum, space and texture, and weighting to obtain an image reconstruction quality score. The method has the beneficial effects that the image quality evaluation precision is greatly improved, and reliable support is provided for subsequent application.

The application provides a kind of urban dissipative space discrimination method and system based on spatial aggregation characteristics, it is related to spatial quality measure technical field.The urban dissipative space discrimination method based on spatial aggregation characteristics, comprising: obtaining city basic geographic space data and its geographic coordinates, form city space information integrated platform;According to the pre-set city dissipative space determination index library, the basic characteristic element data of city dissipative space is calculated, and the basic characteristic element data of city dissipative space is loaded into city space information integrated platform for spatial positioning calibration;Select appropriate grid scale to divide measure area;Each characteristic element data in grid unit is graded assignment;The order of city space is measured to calculate the aggregation;Establish determination rule to obtain four kinds of dissipative space characteristic element data set.The application improves the accuracy and efficiency of city dissipative space identification, and provides targeted guidance and power point for city renewal design and decision control.

The invention discloses a non-reference quality evaluation method for a hyperspectral fusion image based on deep learning, and the method comprises the steps: 1, building a spatial quality evaluation network, and obtaining a spatial quality evaluation result; the spatial quality evaluation network comprises a spatial feature difference extractor and a spatial feature regression device; 2, establishing a spectrum quality evaluation network, and obtaining a spectrum quality evaluation result; the spectrum quality evaluation network comprises a spectrum characteristic difference extractor and a spectrum characteristic regression device; 3, establishing an overall quality evaluation network, and obtaining an overall quality evaluation result; the overall quality evaluation network comprises a spatial feature difference extractor, a spectral feature difference extractor and an overall feature regression device; and step 4, training and testing the space quality evaluation network, the spectrum quality evaluation network and the overall quality evaluation network, and finally performing non-reference quality evaluation on the hyperspectral fusion image after the training is completed. The method solves the problems that an existing image fusion method is poor in accuracy and not comprehensive in evaluation.

The invention relates to an access control community space quality evaluation method and system based on multi-source image data, and the method comprises the steps: extracting residential AOI data from the AOI data of a city as a community boundary, and supplementing the community boundary data in combination with POI data and land utilization data; the method comprises the following steps: automatically acquiring community image data, position and related information data, and preprocessing the data; randomly selecting a certain number of cell image data as a data set, marking the image, training a target detection deep learning model by using the data set, and calculating the model performance; and applying a deep learning model in the cell image data to obtain a spatial quality evaluation result of each cell. Through a Yolo V8 deep learning model and in combination with community image data and street view data of housing resource platforms such as residential guests, deep analysis of access control community space quality is realized. The evaluation efficiency is improved, manpower and material resources are saved, and the unified standard and comparability of evaluation results can be better ensured. The whole process is more automatic and intelligent, the requirement for the professional level of an operator is lowered, and the system is easier to popularize.

The invention discloses a rural public space collaboration evaluation method and system based on continuous track electroencephalogram, and relates to the technical field of human settlement environment space quality measurement, and the method comprises the steps: receiving related data of a rural public space, carrying out the preprocessing of the related data of the rural public space, and obtaining the processed related data of the rural public space, the related data of the rural public space comprises walking sightseeing real-time electroencephalogram data, continuous GPS track data and environment basic geographic space data; based on the processed rural public space related data, extracting a walking brain perception cooperation index, inputting the walking brain perception cooperation index into a pre-established Gaussian process regression model based on a perception cooperation attenuation kernel function, and outputting to obtain a distribution result of the rural public space cooperation degree; and obtaining hierarchical vector transfer data and weight data of the environmental elements, projecting the hierarchical vector transfer data and the weight data of the environmental elements to the rural public space grid units, and performing superposition in combination with the distribution result of the rural public space collaboration degree to obtain a rural public space environmental element weighted collaboration degree distribution result. And clustering to obtain a village advantage collaboration space and a disadvantaged collaboration space.

The present invention discloses a method, system, computer device and medium for measuring the spatial quality of a living street. The method includes: obtaining street view image data of a target area; establishing a street view image database according to the street view image data, and scoring the street view images through a random forest machine learning model to realize the subjective perception of the street view images; obtaining multiple environmental evaluation indicators of the street through visual image semantic segmentation based on a deep learning fully convolutional network and the K-means clustering algorithm, and conducting an objective environmental evaluation of the street view images; assigning weights to the results of the subjective perception and objective environmental evaluation of the street view images according to the analytic hierarchy process, and calculating the measurement result of the comprehensive evaluation of the street. The present invention can accurately measure the spatial quality of urban living streets, has the advantages of high efficiency and easy popularization and application, and can be widely applied to the field of survey and measurement of the spatial quality of urban planning living streets.

This application relates to the field of smart city technology, and in particular to a method, electronic device, readable storage medium, and computer program product for the dynamic configuration of urban service facilities based on multi-source data. The method includes acquiring multi-source data of a target area; generating a human-scale spatial quality score based on dynamically sensed data, obtaining the spatiotemporal distribution characteristics of residents' activities, and calculating the coupling coordination degree between residents' behavior and the spatial environment; constructing an index system of built environment influencing factors at the urban scale based on static spatial data, and identifying key factors and their impact thresholds affecting spatial quality using the human-scale spatial quality score as the target variable; delineating service areas of different levels of living circles based on the spatiotemporal distribution characteristics of residents' activities, coupling coordination degree, key factors, and impact thresholds, and generating optimized configuration schemes and dynamic allocation instructions for public service facilities. This enables quantitative diagnosis of spatial quality and dynamic optimization of facility configuration.

The invention relates to a pattern spot processing method suitable for automatic database building of territorial investigation, and the method comprises the steps: carrying out the optimization processing of each monitoring pattern spot in an obtained territorial investigation basic database, obtaining an optimized monitoring pattern spot, and carrying out the processing of an attribute information set of the extracted optimized monitoring pattern spot, the method comprises the following steps: obtaining standard pattern spots meeting national data standard specifications, performing layer generation and optimization processing on the standard pattern spots to obtain pattern spots after layer optimization, and performing spatial quality inspection optimization processing on the pattern spots after layer optimization to obtain pattern spots after quality inspection optimization; and taking each obtained pattern spot after quality inspection optimization as an ideal pattern spot suitable for constructing a territorial survey database. Thus, a full-process online pattern spot repairing system covering pattern optimization, attribute management and space quality inspection is constructed, automatic pattern spot processing is achieved, and the land survey data processing efficiency and the automation level are remarkably improved.

The application provides a multi-data-source remote sensing image space-spectrum fusion method, device, equipment and medium, which comprises the following steps: acquiring an original image for image space-spectrum fusion; inputting the original image into a multi-data-source remote sensing image space-spectrum fusion network to obtain a fusion image. The method is used to solve the problems in the prior art that spatial deformation and spectral distortion are prone to occur, the training data source is single, the process steps are complicated, the loss of spatial information of the panchromatic image is prone to occur, a certain degree of information distortion exists, and it is difficult to achieve a good balance in the spectral quality and spatial quality of the fusion result. According to the characteristics of the original image, the trained multi-data-source remote sensing image space-spectrum fusion network is used to interactively fuse the spatial features and the spectral features, so that the expression and reconstruction of the features are more accurate and targeted, and the quality of the satellite image output by the multi-data-source remote sensing image space-spectrum fusion network is improved.

The present invention relates to the technical field of oil and gas geophysical data acquisition and processing, and particularly relates to a method for quantitatively evaluating the quality of continuously acquired data of a nodal instrument. The method of the present invention is mainly used for quantitatively evaluating and analyzing the noise in seismic data. Through four-dimensional volume dynamic distribution quality quantitative evaluation and analysis along the ground space direction and along the acquisition time direction, finally, the spatial quality analysis results of all position points within the same time period, the data quality analysis results of all times at the same position point, the moving direction and propagation speed analysis results of the noise source are obtained, so as to quantitatively master the noise distribution characteristics and noise generation rules of the monitoring area and ensure the quality of the continuously acquired data of the nodal instrument.

With the growth of the construction industry and a focus on improving the quality of life in architecture, there is an increasing need for a system to measure the optimal environmental quality of spaces for all users. This is particularly important in today's busy urban life, where factors such as traffic and noise can impact people's well-being. While existing environmental measurement systems can assess many indicators separately, there is currently no comprehensive solution for measuring indicators such as light color, the golden ratio, spatial geometry, and environmental color in architectural spaces. Therefore, this invention is a system to measure all these components for enhancing the comfort and mental well-being of occupants. It measures the crucial indicators such as air quality, tempreture, humidity, sounds, lights, and other features of the place, takes pictures of them and processes them, and also makes suggestions.

The present invention discloses a dynamic multi-exposure light field image fusion method based on structural consistency detection. The method performs high-order singular value decomposition on the angular dimension of the registration light field image corresponding to the light field image in the dynamic multi-exposure light field image sequence to obtain a multi-exposure main base sequence and a multi-exposure non-main base sequence; the multi-exposure main base sequence is input into a main base fusion module based on structural consistency detection to obtain a fused main base; the multi-exposure non-main base sequence is input into the remaining baseband fusion module based on mask partitioning to obtain a fused non-main base; a fused baseband tensor is obtained according to the fused main base and all the fused non-main bases, and is reconstructed by combining the fused baseband tensor and the orthogonal factor matrix of two angular dimensions during high-order singular value decomposition to restore the light field structure, and then the angle information is restored to obtain a fused light field image containing complete angle information; the advantage is that the method can obtain a high dynamic range fused light field image with good spatial quality and angular quality.

The application discloses a kind of limited space quality detection methods based on multi-modal edge cloud transmission, belong to industrial manufacturing and quality detection technical field, innovatively fusion head-mounted camera, flexible hole detection equipment, step gap detection equipment, build millimeter three-dimensional space perception network, cover personnel accessible and inaccessible area, solve the problem of limited vision of traditional manual detection.Detection equipment as data transfer station, real-time processing multi-modal data (video stream, measurement data, voice), realize high-resolution video local decoding and wrist terminal real-time display by FFmpeg technology.CBAM attention mechanism is introduced in the neck part of YOLOv8, and the scratch feature representation is enhanced by channel and spatial attention weighting;SIoU loss function is used instead of traditional IoU, considering the overlapping area, center distance and aspect ratio of the predicted box and the real box, which significantly improves the detection rate of micro-defects.

A split rendering client with means for: sending, to a server, metadata for a virtual camera associated with the client; sending, to the server, a request for adaptation of a gaze-based optimisation profile for a rendering session; receiving from the server an encoded video frame; decoding and displaying the encoded video frames. Also disclosed: a distributed rendering server that uses the metadata and a gaze-based optimisation profile to render, encode and transmit a video frame to the client. Real time eye tracking information may be sent to the server. Gaze-based optimisation profiles may comprise foveated rendering methods and parameters of spatial quality maps used in rendering and foveated encoding. Spatial quality map parameters may comprise size, shape and position of different quality regions, eg. foveal region. Foveated rendering may comprise varying sampling rate during pixel shading, varying or scaling shading rate, varying level of detail with gaze location. Foveated rendering and encoding may comprise spatially varying pixel resolution. Adaptation messages may comprise: metrics indicating profile modification; requests to modify profile parameters; requests to switch to another of plural profiles. Split rendering methods may be used in visualising extended, augmented, mixed or virtual reality on head mounted displays.

The invention discloses a limited space quality detection method based on multi-mode edge cloud transmission, belongs to the technical field of industrial manufacturing and quality detection, creatively integrates a head-mounted camera, flexible hole detection equipment and order gap detection equipment, constructs a millimeter-level three-dimensional space sensing network, covers reachable and unreachable areas of personnel, and improves the detection accuracy. The problem that traditional manual detection view is limited is solved. The edge detection device serves as a data transfer station to process multi-modal data (video streams, measurement data and voice) in real time, and high-resolution video local decoding and wrist terminal real-time display are achieved through the FFmpeg technology. A CBAM attention mechanism is introduced into the Neck part of the YOLOv8, and scratch feature representation is enhanced through channel and space attention weighting; a SIoU loss function is adopted to replace traditional IoU, the overlapping area, the central point distance and the aspect ratio of a prediction frame and a real frame are comprehensively considered, and the microdefect detection rate is remarkably increased.

This invention relates to the technical field of artificial intelligence and urban design, and in particular to an intelligent evaluation method and system for community fitness spaces based on artificial intelligence algorithms. The method includes the following steps: acquiring three-dimensional spatial data, constructing a multivariate dataset, and establishing a three-dimensional model of the fitness space; identifying and disassembling digital assets of the fitness space; restructuring the fitness space assets; and training a benchmark evaluation model for the fitness space; and constructing an intelligent evaluation model for the fitness space. This invention proposes an intelligent evaluation method and system for community fitness spaces using artificial intelligence algorithms in spatial quality assessment. It provides an innovative research framework for the scientific planning and optimized layout of community fitness space construction. This invention is applicable to the quality evaluation of fitness spaces during the design phase, allowing for pre-assessment of spatial quality. Based on the assessment results, targeted design measures can be implemented, reducing subsequent maintenance costs.

The application provides a video super-resolution method and system based on a diffusion model, which comprises the following steps: inputting a video calibration dataset into a pre-trained full-precision diffusion model, performing an inference process, and collecting model running data as calibration data by using a callback function at runtime; determining the contribution degree or sensitivity index of each network layer in the full-precision diffusion model based on the calibration data; performing layer-by-layer quantization resource allocation and federal optimization on the full-precision diffusion model according to the calibration data and the contribution degree or sensitivity index, generating and saving quantization parameters; loading the quantization parameters, performing light-weight inference processing on a low-quality video, reconstructing video details, and obtaining a super-resolution recovery result. The application can still successfully recover a high-resolution video from a low-resolution video in a low-bit quantization scene, and has significant advantages in spatial quality, structural consistency, detail texture recovery and the like compared with other low-bit quantization methods.

The present invention relates to a no-reference spatial quality assessment method and device for remote sensing image fusion. A single-band high-resolution image is obtained from a multi-band high-resolution image. By respectively extracting texture features from the fused image and the single-band high-resolution image, the texture features of the fused image and the texture features of the single-band high-resolution image are obtained. Then, the mutual information metric is performed on the texture features of the fused image and the texture features of the single-band high-resolution image to obtain the feature mutual information between the fused image and the single-band high-resolution image. Furthermore, the spatial quality of the fused image is evaluated without reference based on the feature mutual information. This method has high accuracy and makes the no-reference spatial quality assessment of remote sensing image fusion more in line with practical applications.

The invention discloses a landscape space quality assessment method and device, a terminal and a medium, and relates to the field of landscape visual analysis, and the method comprises the steps: determining an original scene point cloud according to the three-dimensional point cloud data of a to-be-assessed region; carrying out segmentation and vegetation structure extraction on the original scene point cloud, and determining each segmented scene point cloud; converting the segmented scene point clouds into voxel grids, determining the segmented scene point clouds, and extracting voxel related indexes based on the segmented scene point clouds; performing three-dimensional visual simulation on each segmented scene point cloud, and calculating visual related indexes; and determining a landscape quality evaluation result according to the voxel related indexes and the visual related indexes. According to the method, the voxel related indexes and the visual related indexes are extracted based on the three-dimensional point cloud data of the to-be-evaluated area to perform landscape quality evaluation, so that the problem that the visual landscape quality of an ecological space cannot be accurately evaluated due to the fact that a three-dimensional ecological structure cannot be captured and the expression ability of an ecological process is limited in the prior art can be effectively solved.

This invention discloses a dual-domain unsupervised panchromatic sharpening method for remote sensing images based on super-resolution enhancement, comprising: acquiring PAN and LRMS images and preprocessing them; obtaining an LLMS image after preprocessing the LRMS image; constructing a multispectral image super-resolution enhancement sub-network and training it in the low spatial resolution domain; loading the trained multispectral image super-resolution enhancement sub-network and performing super-resolution enhancement on the LRMS image through it to obtain an HLMS image; constructing a frequency detail injection module and inputting the HLMS image and PAN image into the frequency detail injection module for unsupervised learning to obtain an HRMS image. This invention significantly improves the spatial quality of the generated image while ensuring excellent spectral performance, effectively alleviating problems such as spatial detail blurring and object distortion in the generated image.

The invention discloses a multi-modal image fusion method based on space-frequency interaction. The method comprises the following steps: step 1, inputting an infrared image and a visible light image; 2, performing preliminary coding and feature extraction on the infrared image and the visible light image by using a basic convolutional neural network; step 3, performing depth space feature extraction on the preliminarily extracted space features of the infrared image and the visible light image, and then fusing the space features of the infrared image and the visible light image; step 4, frequency feature extraction of two stages is carried out, feature interaction fusion between modals is carried out in the first stage, and frequency enhancement in the modals is carried out in the second stage; step 5, performing cross-domain feature fusion on the spatial features and the frequency features after fusion of the infrared image and the visible light image; step 6, performing feature decoding; and step 7, image reconstruction is carried out to obtain an output image after fusion of the infrared image and the visible light image. According to the invention, the space quality of the fused image and the richness of information are effectively improved.

The invention provides an intelligent house resource inspection method and system based on computer vision and multi-sensor fusion, and belongs to the field of intelligent house inspection. The method comprises the following steps: acquiring multi-modal perception data such as a visual image, a three-dimensional point cloud, environmental parameters and equipment operation, performing space-time registration and feature fusion, and constructing a multi-dimensional house inspection feature set; in combination with defect identification and parameter deviation analysis, establishing a spatial quality coupling model, mining cross-modal relevance, and generating a defect risk mapping network; a dynamic defect prediction model is introduced to predict and evaluate the defect evolution trend and deterioration degree, and a housing resource quality risk map is formed; establishing a multi-dimensional quality evaluation system, realizing comprehensive grade evaluation of the risk map, and outputting a structure test room report; and rectification decision suggestions are generated based on the report, and on-site real-time early warning and scheme pushing are realized in combination with edge calculation, so that automation, intelligence and perspectiveness of the house inspection process are realized, and the detection precision, the risk pre-judgment capability and the management efficiency are improved.

A video generation device obtains Laplacian pyramid levels for 360° video frames; promotes each obtained Laplacian pyramid level to restore a corresponding set of Gaussian pyramid levels for each 360° video frame; and, for each pixel in each 360° video frame, determines a restored Gaussian pyramid level from the restored set of Gaussian pyramid levels based on a live broadcast bitrate budget and a viewport area by executing a pyramid level allocation process. A 360° video with heterogeneous spatial quality is synthesized by aggregating the restored Gaussian pyramid levels determined for each pixel in each 360° video frame. Therefore, when a user views the 360° video, the bitrate can be reduced while providing the user with high video quality. Furthermore, the present invention relates to a download agent including such a video generation device, a corresponding method, and a computer program.