25 results about "Density analysis" patented technology

The invention discloses an intelligent-based abnormal carbon emission data automatic identification early warning system, which belongs to the technical field of intellectualization and comprises a data acquisition preprocessing module, a high-precision space-time analysis module, an intelligent analysis module, an automatic abnormal identification module, a carbon footprint tracing module, a self-adaptive adjustment module and an intelligent emission prediction module. A carbon emission source and time-space distribution characteristics are accurately positioned through spatial positioning and time sequence analysis of the high-precision time-space analysis module, a spatial distribution diagram is more detailed and accurate through the optimized sensor position and an interpolation algorithm, and time sequence analysis is more accurate through timestamp correction and power spectrum density analysis. The method effectively evaluates the periodic intensity of the signal, analyzes a hidden mode and an association rule in the data through the intelligent analysis module integrating the spatial-temporal characteristics and the related information of the data acquisition and preprocessing module, analyzes the spatial-temporal association between variables through the calculation of a clustering center and the association intensity, and facilitates the discovery of a potential abnormal mode.

The invention discloses a shallow sea area water depth inversion method based on a satellite-borne laser radar, and particularly relates to the technical field of single-photon water depth information extraction. The denoising method comprises the following steps: S1, data preprocessing: reading data, carrying out visual presentation, dividing photons into offshore photons, sea surface photons and undersea photons, and intercepting an effective photon region; s2, coarse denoising: denoising the area intercepted in the step S1 by adopting a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; s3, fine denoising: screening effective signals of sea surface photons by adopting a Gaussian fitting algorithm, removing sea photons, separating subsea photons from the sea surface photons, performing density analysis on the subsea photons, extracting a density peak point, and constructing a density trend curve of the subsea photons by adopting a cubic spline interpolation method, eliminating residual noise deviating from normal distribution according to the trend curve; and S4, performing water depth inversion: performing refraction correction on the subsea photons subjected to fine denoising, fusing the corrected subsea photons and the sea surface photons subjected to fine denoising, and generating a water depth distribution diagram of the shallow sea area.

The invention discloses an intelligent monitoring and early warning method and system for a dangerous large project on a building construction site, and the method comprises the steps: carrying out the fusion processing of multi-source data, such as displacement, inclination and axial force, collected by edge Internet of Things equipment, extracting the characteristics of structural deformation and load change, and constructing a comprehensive situation representation; a high-risk area and a safety area of a construction site are identified by adopting clustering analysis, a spatial proximity relationship between the areas is established, a risk coupling degree is calculated, and a differentiated monitoring strategy is delimited according to the spatial proximity relationship; performing time sequence density analysis on the abnormal event data, fitting a risk evolution curve, positioning a situation mutation node, and establishing association mapping of construction conditions and risk steps; according to the method, a transmission path is tracked based on a risk attenuation rule, a preliminary early warning sequence is generated, an early warning trigger threshold value is determined through adaptive grading and boundary matching, a graded early warning instruction is output after gradient verification, and timely and accurate early warning information can be provided for safety management and control of dangerous engineering.

The invention discloses an anomaly detection and identification method for an urban building facade, relates to the technical field of building maintenance, solves the problem that a single detection technology, such as infrared thermal imaging detection or pure visual imaging detection, is obviously short, and locks a standard range through infrared thermal imaging and temperature value section density analysis. The method comprises the following steps of: accurately marking suspected abnormal partitions, matching and associating standard image areas through position vectors, and matching with height difference verification of three-dimensional point cloud scanning to realize multi-level accurate screening of the abnormal partitions, so that the problems of misjudgment and missed judgment of a single detection mode are effectively avoided; according to the method, field temperature data is dynamically determined, dependence on fixed parameters is not needed, the method can adapt to different types of building facade detection scenes in different environments, and the universality of the method is greatly improved.

The invention relates to the technical field of geological disaster risk assessment and geographic information systems, in particular to a disaster-inducing factor dynamic quantification and characterization method based on an entropy evaluation method. The method comprises the steps of constructing a disaster-inducing factor data set, performing standardization processing, performing dynamic weight calculation through an entropy evaluation method, and performing quantitative characterization and grading. The method comprises the following steps: firstly, based on multi-source data, integrating historical data of four disaster types of mountain torrent, debris flow, landslide and collapse and related disaster-inducing factors to form a time-space associated unified database; then, min-max normalization and disaster area density analysis are adopted, and factor dimension differences are eliminated; dynamic updating of the factor weight is realized by calculating the information entropy and the difference degree; and based on a natural breakpoint method, carrying out scientific grading on the standardization factors, and combining entropy weight to construct a dynamic quantitative index system. According to the method, the problem that a traditional static weight method cannot reflect dynamic change of disaster-inducing factors along with time is solved, and the timeliness and accuracy of disaster sensitivity assessment are improved.

The application relates to a land space multi-level function data analysis processing method and device, and belongs to the technical field of land space distribution data processing. The application solves the problem of inconsistent function positioning of different types of land space in the existing land space function analysis method and the problem of insufficient accuracy of analysis result data. The land space multi-level function data analysis processing method and device of the application update the function recognition unit of a plot according to a use-function conversion matrix; quantize the spatial distribution density of each type of POI of the plot according to the kernel density of POI data; calculate the distribution frequency of different POI types in the function recognition unit in each function category; combine the kernel density analysis result and the frequency density analysis result to perform weighted calculation and standardization processing on the spatial function value of each function recognition unit; and determine the data analysis result of each function recognition unit in each function level framework. The application improves the accuracy of the analysis result data by analyzing and processing the layered land space.

In order to overcome the defects in the prior art, the invention provides a method and a device for regulating and controlling the density of a primary blank in situ and a density analysis method, according to the density analysis method, the density of the primary blank is analyzed under the dual effects of in-situ vibration and negative pressure, and at the moment, the density of the primary blank is obtained through analysis of an analysis model. Based on the analysis model, a method for increasing the density of the initial blank can be obtained, so that the density of the initial blank is subjected to in-situ regulation and control by applying the vibration frequency, the in-situ vibration amplitude and the negative pressure. In addition, the invention provides a device for realizing in-situ regulation and control of the density of the initial blank, and the device determines the vibration frequency, the vibration amplitude and the negative pressure value based on the density analysis method and provides corresponding vibration and negative pressure environments for the powder bed in the printing process, so that the initial blank with the density close to the preset density is obtained.

The invention provides a boundary detection method, device and equipment based on point cloud data, and a medium, is used for the technical field of boundary anomaly detection, and can solve the problems of low efficiency and accuracy of existing boundary anomaly detection. Comprising the steps of performing plane fitting on multiple pieces of target point cloud data to obtain an initial enclosure column point cloud and an initial enclosure surface point cloud, and performing local density analysis on the initial enclosure surface point cloud to obtain a target enclosure surface point cloud; performing grid division on the target surrounding surface point cloud, and determining an abnormal surrounding surface grid according to the point cloud number of each point cloud grid and a preset number threshold; clustering the initial enclosing column point clouds to obtain a plurality of target enclosing column point clouds, and determining an inclination angle between each target enclosing column point cloud and the gravity vector; determining an abnormal enclosing column point cloud according to each inclination angle and a preset angle threshold value, and determining an enclosing boundary detection result according to the abnormal enclosing surface grid and the abnormal enclosing column point cloud; and the accuracy and efficiency of boundary anomaly detection are improved.

The invention relates to an integrated system that enables the determination of the most suitable locations for cargo service centers, the optimization of responsibility areas, and the efficient planning of distribution routes in the logistics and cargo transportation sector The system operates based on data received from the user, digital map data provided by the geographical information systems module (30), dispatch data (100), and dynamic parameters. The service center positioning module (40) performs density analyses and customer address clustering to determine the most suitable service center locations. The route optimization module (60) optimizes distribution routes by using dynamic data such as traffic density and delivery durations. The simulation module (70) evaluates system performance by testing alternative scenarios, and the visualization tool (110) presents analysis results to the user (10) on a dynamic map. Through this structure, time and cost optimization is achieved, thereby increasing overall logistics efficiency.

The invention discloses a formation characteristic while-drilling identification method, system and equipment based on drilling parameters and a medium, and relates to the technical field of petroleum and natural gas drilling engineering. The method comprises the steps that firstly, small layers are classified according to element logging results of adjacent wells, drilling parameters and micro drilling speed data are collected, and a time-drilling parameter-micro drilling speed matrix is constructed; on the basis, the while-drilling vibration data is divided into different magnitudes through a clustering method, and the characteristics of each vibration magnitude are quantified by combining abrupt change point density analysis. Then, an improved deep convolutional neural network algorithm is utilized to establish a correlation model of the catastrophe point density and the small-layer micro drilling rate data of the stratum; and finally, dynamically judging the stratum type in the drilling process by calculating the correlation coefficient of the micro drilling speed and the abrupt change point density in real time, thereby realizing accurate stratum identification. The method can improve the accuracy of while-drilling identification of the small layer of the horizontal section stratum, and is suitable for while-drilling identification under different geological conditions.

The invention discloses a method and a system for accurately identifying a irrigated land based on space-time two-dimensional feature fusion, and belongs to the technical field of remote sensing image processing and agricultural information. The method comprises the following steps: acquiring multi-source data of a target area, wherein the multi-source data comprises a remote sensing image sequence, surface water source spatial distribution data, underground water irrigation facility spatial distribution data and cultivated land pattern spot data; spatial analysis is carried out based on water source and irrigation facility data, and an irrigation condition spatial distribution diagram is generated through Euclidean distance calculation and nuclear density analysis; extracting a time sequence vegetation index of the cultivated land pattern spot data in a moisture sensitive phenological period based on the remote sensing image sequence, and calculating a stability index; constructing a deep learning model fusing the spatial semantic segmentation network and the time sequence analysis network, and performing model training by taking the spatial distribution map and the stability index as fusion input features; and identifying and outputting a irrigated land distribution map by using the trained model. The invention further discloses a corresponding system, electronic equipment and a storage medium. According to the invention, through time-space two-dimensional feature fusion, the precision of irrigated land identification is effectively improved, and the limitation of a single-dimensional method is solved.

The invention discloses a high mountain and valley region landslide hazard hidden danger identification method based on an SBAS-InSAR technology, and the method comprises the steps: obtaining a time sequence satellite image of a target high mountain and valley region, and carrying out the preprocessing of the time sequence satellite image, and obtaining the processed data; performing SBAS-InSAR deformation monitoring on the processed data to obtain an annual deformation quantity and an annual deformation rate; constructing a disaster grading grade based on the annual deformation rate; performing kernel density analysis processing on the deformation point data extracted by the SBAS-InSAR, and obtaining a high-risk area in combination with a preset high-definition image and the disaster analysis level; according to the method, freeze-thaw data of a high-risk area is extracted, a linear regression model is constructed, the freeze-thaw data is analyzed based on the linear regression model to obtain a landslide hidden danger result of the high-risk area, and the precision and reliability of deformation monitoring of the alpine and valley area are improved by combining an SBAS-InSAR technology with frozen soil freeze-thaw period data.

The invention provides a multi-source data fused small and micro wetland value evaluation method and system, and relates to the technical field of ecological environment monitoring and remote sensing, and the method comprises the steps: collecting multi-source remote sensing data of a research area, and extracting small and micro wetland pattern spots from the multi-source remote sensing data; performing index evaluation on the small and micro wetland pattern spots based on a kernel density analysis method and landscape indexes to obtain landscape structure and connectivity value indexes; inverting ecological function parameters corresponding to the small and micro wetland pattern spots according to the time sequence remote sensing data to obtain ecological function and specific value indexes; calculating social service and proximity value indexes based on the multi-source remote sensing data and the interest points and media data of the small and micro wetland pattern spots in a preset service radius; and constructing a small and micro wetland value evaluation comprehensive index system based on the plurality of value indexes, calculating a comprehensive value index, and generating a small and micro wetland value grade map according to the comprehensive value index. According to the method, the refinement degree of the value evaluation result of the small and micro wetlands can be improved.

The invention relates to the field of shield tunneling machine systems, and discloses a control method of a quasi-rectangular shield tunneling machine, which comprises the following steps: acquiring section form data of the shield tunneling machine through a laser scanning device to obtain real-time section form data of the quasi-rectangular shield tunneling machine; performing section form difference analysis on the real-time section form data of the quasi-rectangular shield tunneling machine to obtain section form difference data; carrying out distribution density analysis on section form differences according to the section form difference data to obtain section form difference distribution density data; according to the method, by analyzing the cross section form difference of the shield tunneling machine in real time, the deviation between geological changes and equipment behaviors in the operation process of the shield tunneling machine can be found in time, then rapid adjustment is made, and the working efficiency of the shield tunneling machine is improved. Therefore, unnecessary downtime can be reduced, the propelling process of the shield tunneling machine is optimized, and the construction efficiency is improved.

The invention relates to the technical field of automatic identification and early warning information, in particular to an automatic identification and early warning method of an internet of things electric power warning board for preventing external damage, which can acquire surface image data and light reflectivity of the electric power warning board through a light sensor and transmit the data and the reflectivity to an internet of things platform, comprehensively analyze insect coverage density and thermal anomaly, and improve the early warning accuracy of the electric power warning board. Aiming at thermal abnormity of the electric warning board caused by local shielding due to gathering of insects at night, an insect coverage density distribution diagram is generated through image segmentation and density analysis, the shielding degree is evaluated by combining light reflectivity, surface and light source area temperature data are extracted, and a high-temperature area and a high-density insect coverage area are detected in an overlapping manner to obtain a high-density insect coverage area. The correlation between the density increment and the temperature rise amplitude is analyzed, a thermal anomaly index is generated, then a hot spot distribution diagram is divided, a grading early warning signal is triggered, the Internet of Things platform sends early warning to the remote terminal according to the hot spot distribution diagram, and the field control system is linked to respond to the anomaly.

The application claims a map generation method based on laser radar point cloud density analysis, comprising a modeling stage and a mapping stage. The modeling stage establishes a map model and a radar density model, wherein the map model contains the geometric information and spatial density information of the terrain, and the radar density model is used to represent the spatial density normalization coefficient of the point cloud. In the mapping stage, first, the laser radar point cloud data is preprocessed; second, the voxel point cloud occupancy density is calculated, and the spatial density representation is obtained by combining the density normalization coefficient; the step height feature is obtained by calculating the height difference between adjacent voxel columns; the local terrain region division strategy is used to fit the plane of each region to extract the slope and roughness attributes; and finally, the above elements are integrated to obtain a similar elevation map. The method can effectively express the spatial density information and multi-dimensional description of the terrain features in a non-structured environment, and improve the path planning efficiency and safety of the ground intelligent mobile platform.

The application discloses a large-scale seismic signal acquisition equipment consistency detection method, device and system, and relates to the technical field of geophysical exploration. The method is executed by a computer device in a large-scale seismic signal acquisition equipment consistency detection system, and after receiving background noise original data continuously and synchronously collected by a plurality of seismic signal acquisition equipment, the cumulative power spectrum variance value of each acquisition equipment is obtained through pretreatment and multi-step calculation in sequence, and finally, according to the cumulative power spectrum variance value of each acquisition equipment and the distance from each acquisition equipment to the center position, the acquisition equipment with abnormal relationship in the relationship dimension of the cumulative power spectrum variance and the collection time or the relative distance is determined. Through the noise power density analysis of the background noise data, the consistency detection of the large-scale seismic signal acquisition equipment can be realized efficiently, reliably and automatically, and the detection result can accurately reflect the performance state of the seismograph.

This invention relates to the field of fishing vessel monitoring technology, specifically to a catch information acquisition system for fishing vessels using light-covered nets based on AI cameras. The system includes: a reflective point extraction module, a trajectory fitting module, an occlusion classification module, an edge redrawing module, and a density analysis module. In this invention, reflective points on the surface of underwater fish are extracted and high-brightness areas are selected to construct a set of gloss response points. The movement trajectory of the reflective point group is analyzed using a multi-frame image sequence. A random sampling consensus algorithm is used to fit the path features, identifying the continuity of the trajectory and changes in the movement pattern. An occlusion state classification is performed on the path features based on a support vector machine model, determining whether it is extended occlusion, cross occlusion, or boundary detachment. Based on the classification results, the edge contours of the reflective points are aligned with direction vectors and redrawn using path matching to restore the contours lost due to occlusion. By analyzing the brightness change trajectory of the fin region in the redrawn edge path, multi-granular data support is provided for catch statistics.

The invention provides a crowd density detection method and system based on a three-dimensional geographic information scene, and the method comprises the steps: calling a crowd density detection artificial intelligence model bound with the three-dimensional geographic information scene, inputting three-dimensional geographic information scene data and corresponding continuous image data, and obtaining an initial crowd density feature; performing correlation calibration on the initial crowd density features and the scene structure features to obtain calibrated crowd density features; based on the calibrated crowd density features, triggering model scene adaptation adjustment to obtain an adapted model; performing progressive density analysis on the continuous image data through the adapted model to obtain a detection result; and a detection result is fed back to the model feature calibration module to realize optimization. According to the method, three-dimensional scene information is fully utilized, the limitation of a two-dimensional image is overcome, the three-dimensional distribution of the crowd can be accurately reflected, the detection accuracy and generalization ability are improved through correlation calibration and scene adaptation, and the detection precision and stability are further improved through progressive analysis and feedback optimization.

The present application belongs to the technical field of seismic data processing, and discloses a method for improving the accuracy of root mean square velocity in a complex structure area, comprising the following processes: S1, establishing a reference root mean square velocity field; S2, interpreting a target layer and then performing along-layer velocity analysis to obtain an along-layer analyzed velocity field; S3, combining the along-layer analyzed velocity field with the reference root mean square velocity field to obtain a target layer controlled root mean square velocity field; S4, using the target layer controlled root mean square velocity field to perform longitudinal high-density root mean square velocity analysis and automatic picking; S5, calculating a difference root mean square velocity field; S6, obtaining a processed difference root mean square velocity field; and S7, obtaining an optimized high-density analysis root mean square velocity field. The present application uses along-layer high-density velocity analysis under the constraint of a geological structure layer to perform vertical high-density velocity analysis, thereby improving the accuracy of the velocity field and achieving the accuracy of complex structure and low-amplitude structure imaging.

The invention provides an archaeological site prediction model construction method and device and electronic equipment, and the method comprises the steps: obtaining archaeological environment factor data and site point location data of a preset region, and carrying out the kernel density analysis of the site point location data, so as to generate a density grid map layer; target areas with dense site points are removed from the density grid layer, and negative sample points are generated in the remaining areas; and constructing a sample set based on the negative sample points, the ruins point location data and the archaeological environment factor data, training different types of candidate ruins prediction models by using the sample set, and selecting an archaeological ruins prediction model from the trained different types of candidate ruins prediction models. By adopting the archaeological site prediction model construction method and device and the electronic equipment, the prediction precision of the archaeological site prediction model in the archaeological site prediction process is improved.

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.

The invention relates to the technical field of data processing, and provides an innovation-driven industrial park identification method and system based on multi-source data fusion, and the method comprises the steps: obtaining enterprise basic data, technical innovation data, supply chain dynamic data, talent ecological data, green development data, geographic space data and other multi-source data of a to-be-identified region; performing data preprocessing including data association, spatial function analysis and kernel density analysis on the multi-source data; determining a target data index of the to-be-identified area based on the preprocessed data; based on the target data index of the to-be-identified area, determining an innovation driving intensity index of the to-be-identified area; and inputting the innovation driving intensity index, the target data index, the cross-regional radiation capability and the technical reserve depth of the to-be-identified region into the innovation driving type industrial park identification model to obtain a park type output by the innovation driving type industrial park identification model. According to the invention, the identification accuracy of the innovative driving type industrial park can be effectively improved.

This invention relates to a mineralization prediction method based on rare data synthesized from mineralization spatial structure. The method involves collecting and preprocessing geological data based on prediction requirements; constructing a mineralization prediction model based on the geological background of the study area, determining prediction variables, and creating prediction layers; performing mineralization spatial structure analysis, and synthesizing a preset number of rare data prediction variables based on the prediction variables of the study area to generate several training datasets; training the constructed mineralization prediction model using these training datasets to predict the probability of mineralization in the study area; evaluating the performance of the mineralization prediction model, and delineating prospecting target areas based on the prediction results of the best-performing model. By employing the mineralization prediction method disclosed in this invention, kernel density analysis of the spatial distribution of known ore deposits can synthesize a certain number of positive samples while taking into account geological context, which is beneficial for training the mineralization prediction model and improves the accuracy of prospecting prediction.

The invention belongs to the technical field of three-dimensional measurement, and particularly discloses a ship-borne block stone measurement automatic measurement method based on laser radar point clouds, which comprises the following steps of: respectively scanning a no-load ship and a full-load gravel ship through SLAM (Simultaneous Localization and Mapping) equipment fixed on a mechanical arm of a positioning ship to obtain point cloud data; the data is subjected to denoising processing, deck plane features are extracted, and four-ship point cloud preliminary registration is achieved through main direction alignment with the two positioning ships as the benchmark; after point cloud accurate registration of the four ships is achieved, positioning ship data are removed through segmentation of a minimum enclosing rectangle and a cutting line, and fine registration is conducted on the two ships; according to the height difference, extracting a rock block area point cloud; performing density analysis and grid down-sampling on the rock block point cloud, generating a three-dimensional grid model for the sparse point cloud and the dense point cloud by adopting different parameter values, and repairing the cavity and calculating the volume after fusion. According to the invention, high-precision automatic measurement of shipborne block stone measurement can be realized.