11 results about "Soil classification" patented technology

The invention relates to the technical field of roadbed detection, and discloses a roadbed compaction quality detection method based on ultrasonic wave collaboration, and the roadbed compaction quality detection method comprises the following steps: collecting full-wave band ultrasonic echo signals, and obtaining current environment data; calculating a longitudinal wave speed, a transverse wave speed and a wave speed ratio, and extracting an attenuation coefficient and a frequency dispersion characteristic of the ultrasonic echo signal to obtain an acoustic characteristic set; inputting the acoustic feature set and the environmental data into an environmental compensation model, outputting a standard acoustic parameter set subjected to temperature, humidity and water content correction, analyzing the standard acoustic parameter set through a soil texture classification algorithm, and judging the current soil texture type; and combining the standard acoustic parameter set and the current soil texture type into a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a compactness calculation model, and calculating to obtain a roadbed compactness value of the current measuring point. According to the method, high-precision compaction degree numerical calculation is realized, and intelligent and visual whole-process support is provided for roadbed construction quality.

The invention relates to the technical field of computer data processing, and provides a soil classification prediction method and system based on environment and remote sensing multi-source data. The method comprises the following steps: constructing a soil characteristic data set containing spectral characteristics, physicochemical characteristics and environmental characteristics; calculating the contribution degree of each feature to soil classification and the correlation degree between the features, generating an initial weight configuration, updating a weight coefficient according to the contribution degree of the spectral feature and / or the correlation degree between the physicochemical feature and the environmental feature, generating an adaptive weight configuration, and based on the adaptive weight configuration, carrying out weighted fusion on a feature subset of which the contribution degree exceeds a contribution degree threshold value, so as to obtain an adaptive weight configuration; generating a soil classification feature vector, constructing a soil classification decision tree based on the soil classification feature vector, and determining an optimal classification path; and predicting a classification result of the to-be-classified soil sample based on the optimal classification path, if the confidence of the classification result does not meet a confidence threshold condition, updating the weight coefficient, and returning to the step of generating the adaptive weight configuration until the confidence threshold condition is met, thereby obtaining a final classification result.

The application discloses a method for obtaining soil strength indexes based on soil layer geophysical prospecting data, and comprises the following steps: S1, obtaining the test depth and equivalent shear wave velocity of different measuring points in a target site; obtaining the drilling samples of each measuring point at the corresponding depth, obtaining the corresponding internal friction angle and soil type; preprocessing the collected data to obtain a comprehensive data set; S2, selecting the test depth and equivalent shear wave velocity in the comprehensive data set as basic features, constructing derived features, performing feature standardization, and obtaining a data set; dividing the data set into a training set and a test set; S3, based on the training set obtained in S2, optimizing and training the soil classification model and the internal friction angle prediction model; S4, obtaining the soil strength index internal friction angle based on the soil classification model and the internal friction angle prediction model obtained by the training in S3. The method of the application realizes high-precision prediction of soil strength indexes by constructing derived features and introducing physical range constraints.

The invention discloses a soil hardness prediction method and device, equipment, a storage medium and a program product, and relates to the technical field of image data processing, and the soil hardness prediction method comprises the steps: determining soil attribute data, predicting soil humidity time sequence data through the attribute data, carrying out the soil classification, and finally determining the soil penetration resistance. Comprehensive calculation of the soil hardness is achieved, meanwhile, the change trend in a period of time in the future can be reflected, and the timeliness and the refinement degree of drawing are remarkably improved. Meanwhile, widely available environmental data (such as satellite remote sensing, digital elevation models and meteorological reanalysis products) are used as input and can be quickly applied to areas lacking measured data, the data acquisition cost and time are greatly reduced, and the method has the capability of being popularized and applied to large-scale areas (such as provincial levels and drainage basins).

The application relates to the field of remote sensing image processing, in particular to a remote sensing image data acquisition method for farmland soil type classification, which comprises the following steps: periodically acquiring remote sensing image data of a target region according to a preset period; obtaining distance measurement between any two pixel points in each remote sensing image, segmenting each remote sensing image to obtain a ground object region; analyzing the trend and amplitude of the spectrum curve of each pixel point to determine the red edge performance degree of each pixel point; determining the vegetation growth performance degree of each pixel point; determining the farmland soil segmentation quality of each remote sensing image; screening a to-be-classified image from all remote sensing images of the target region, screening a farmland soil region to construct a bare soil mask image, and combining a neural network to perform farmland soil classification on the ground object region in the to-be-classified image. The application aims to accurately divide the farmland soil region in the remote sensing image and improve the accuracy of farmland soil type classification.

The application discloses a soil undisturbed soil sampling device for black soil classification and partitioning, which is used for sampling deep soil A and relates to the technical field of soil sampling, and comprises a guide cylinder, which is obliquely extended into deep soil, a side opening is formed in the side wall of the guide cylinder, the side opening is located below A, and the axis of the side opening passes through A; a position adjusting assembly, which has a position adjusting end, the position adjusting end can adjust the depth of the extension into the guide cylinder; an adjuster, which is fixed to the position adjusting end and has an extension end, the extension end can move along the radial direction of the guide cylinder; and a sampling assembly, which is arranged on the extension end, the application avoids the process that the sampling tool penetrates the soil layer above the target sampling point, maximally eliminates the disturbance to the soil at the target sampling point, and maximally maintains the undisturbed structure of the soil.

The invention discloses a soil layer division method and system based on an adaptive PELT change point detection algorithm, and the method comprises the steps: carrying out the iterative calculation based on the conical tip resistance and side wall friction resistance, and obtaining a soil body classification index, the normalized conical tip resistance, the normalized friction ratio and a stress index; based on the soil body classification index sequence, adaptively determining a penalty coefficient and a minimum layering thickness parameter of a PELT change point detection algorithm to obtain a parameter-optimized PELT change point detection algorithm; adjusting the position of the interface in combination with a depth weighting method to obtain an optimized soil layer interface depth sequence; and determining and outputting the type of each layer of soil in combination with a soil layer classification standard. According to the method, dynamic optimization of parameters is realized based on combination of static sounding data, soil body classification indexes and a parameter-adaptive PELT change point detection algorithm, and the adaptability of the algorithm to different soil layer conditions is improved; and meanwhile, through a depth weighting method, the interference of data noise on interface positioning is effectively reduced, and the precision and anti-noise capability of interface positioning are remarkably improved.

This invention belongs to the technical field of geotechnical engineering and earthquake engineering, and provides a method based on CPT and V s The joint testing method for liquefaction assessment, which considers the geological age effect, can quantitatively characterize the geological age effect of soil, combine the advantages of CPT and Vs tests, and does not require precise soil age information. The technical solution adopted includes the following steps: S1, in-situ test data acquisition, including static cone penetration test and shear wave velocity test; S2, parameter normalization processing, calculating normalized cone tip resistance and normalized shear wave velocity, and then introducing the soil classification index to obtain the normalized cone tip resistance of equivalent pure sand; S3, establishing the correlation between benchmark static cone penetration test and shear wave velocity; S4, determining the geological age aging factor of soil; S5, constructing a comprehensive evaluation index considering geological age; S6, establishing a liquefaction assessment model; S7, determining the liquefaction discrimination probability threshold; S8, evaluating the site liquefaction potential.

The application discloses a soil classification and map compilation method, and particularly relates to the technical field of soil investigation and remote sensing image processing, and comprises the following steps: acquiring remote sensing images, surface images, sampling point data and elevation data of a target region; performing resampling and spatial position alignment on the remote sensing images and the surface images, and projecting the sampling point data and the elevation data into an image coordinate system after alignment to output scene data; calculating spectral difference values, texture difference values, slope difference values and coverage difference values according to the scene data according to image positions, extracting regions with difference values and continuous regions larger than surrounding image elements, and outputting candidate boundary zones; and positioning through multiple sources, extracting the candidate boundary zones, accumulating differences on both sides of the zonal units, and connecting soil boundaries, wherein the scheme further constrains the grid recognition result to a continuous boundary formation process, relatively suppresses patch fragmentation, boundary swing, mis-cutting and mis-splicing, and is beneficial to stably converting the soil recognition result into soil boundaries which can be directly compiled into maps.

This invention belongs to the technical field of geotechnical engineering and earthquake engineering, and provides a site seismic delineation method based on the correlation between static cone penetration test (CPT) and shear wave velocity, fully combining CPT and V s The advantages of the two testing methods are that by introducing characteristic parameters reflecting the degree of soil aging, a more reasonable classification of seismic sites can be achieved, thereby improving the scientific nature of site classification and the reliability of seismic design. The method includes the following steps: S1, in-situ test data acquisition, including static cone penetration test and shear wave velocity test; S2, parameter normalization processing, calculating normalized cone tip resistance and normalized shear wave velocity, and then introducing soil classification index to obtain the normalized cone tip resistance of equivalent pure sand; S3, establishing the correlation between benchmark static cone penetration test and shear wave velocity; S4, determining the soil geological age aging factor; S5, constructing a comprehensive site seismic classification index; S6, site seismic category classification; S7, engineering application and result output.

The invention relates to the technical field of earth pressure balance shield construction, in particular to an earth pressure balance shield improved muck classification evaluation method and system. According to the method, unsupervised clustering analysis is carried out by comprehensively considering multiple target judgment indexes related to muck improvement, and an improved muck classification system comprehensively considering the influence of multiple factors is established. The index interval value corresponding to each improved muck category is determined through probability density analysis, the improved muck with different physical and mechanical properties can be effectively distinguished, a clear quantitative reference is provided for subsequent muck improvement effect classification prediction based on improved muck classification, and the accuracy of soil pressure balance shield improved muck classification evaluation is improved. And finally, constructing a classification prediction model by using a limit gradient lifting algorithm, predicting the improved muck category to which the improved muck obtained by the muck improvement scheme to be detected belongs, and realizing accurate classification evaluation of the muck improvement effect.