93 results about "Convex hull algorithms" patented technology

The invention provides a three-dimensional object collision detection parcel grid generation method and system, a storage medium and a program product, and the method comprises the steps: extracting all vertex data of a first object according to a sub-Mesh data set after serialization of a three-dimensional model, and determining an initial center point; each vertex of each piece of sub Mesh data is converted from a local coordinate system to a global world coordinate system, and a point cloud data set is generated; constructing an initial convex hull by adopting a matrix hierarchical extremum retrieval algorithm, and generating a final convex hull by adopting a conflict graph and incremental convex hull algorithm; initializing a radius coverage model by taking the geometric center of the model as a sphere center; performing subdivision according to longitude and latitude to generate a hierarchical ball grid; emitting rays, correcting vertex positions layer by layer, approaching the surface of the model, and outputting a collider network. Through the overall point cloud set, the incremental convex hull algorithm and the spherical grid contraction technology, unified topological wrapping of the multiple three-dimensional objects is achieved, the visual fidelity and the collision precision are ensured, and resource consumption is reduced.

A method for modeling acoustics in a delimited area, i.e., a computer-implemented method of modeling acoustics of an object as a numerical simulation model to determine field variables within a delimited control volume, the method comprising: (a) providing a coordinate system mapping positions in the control volume (b) providing a geometric specification of a sound source in the control volume based on the coordinate system. The objective of the invention is to improve the efficiency of numerical modeling of a transient (vibration) acoustic wave propagation phenomenon in a boundary-free domain. Additional steps are proposed: (c) constructing a convex shape around a sound source using a fast convex hull algorithm (d) stretching an infinite element starting from the convex shape (e) constructing a field shape function (g) using auxiliary coordinates by mapping radial coordinates of a coordinate system to auxiliary coordinates extending from the convex shape to the infinite element to transform the coordinate system into a field variable coordinate system (f1) Determining field variables by solving a numerical simulation model provides a computer system for simulating a system by applying a computer-implemented method.

The invention discloses a method for automatically calculating the over-excavation and under-excavation amount of a tunnel based on a Monte Carlo method, and belongs to the field of tunnel intelligent construction and blasting quality evaluation. According to the technical scheme, the method comprises the steps of obtaining a tunnel bare surrounding rock point cloud, and preprocessing point cloud data; generating a designed tunnel contour point cloud and registering the designed tunnel contour point cloud with an actual tunnel point cloud; convex hulls are generated for the two point clouds by using a convex hull algorithm, and a cuboid boundary box capable of wrapping the two convex hulls at the same time is established; a large number of points are randomly generated in the boundary box, and the position relation between the points and the two convex hulls is judged, so that it is determined that the points fall in an over-excavation area or an under-excavation area; by counting the ratio of the number of random points in the back-break area to the total number, the ratio of the irregular back-break area to the volume of the bounding box is obtained, and the ratio is multiplied by the volume of the bounding box to obtain the area body, so that the back-break amount which is difficult to measure and calculate in tunnel quality evaluation is accurately and quickly obtained. The method has the beneficial effects that the tunnel construction cost is reduced, and the construction efficiency and the safety of measurement personnel are improved.

The invention discloses a natural growth apple three-dimensional attitude recognition method based on a real-time multi-task fusion algorithm, and the method comprises the steps: obtaining an aligned RGB-D image through a depth camera, constructing a data set in combination with an image enhancement technology, and synchronously training a classification, segmentation and key point detection model through a multi-task deep learning framework; judging the contour form of the apple through the classification model, and carrying out contour fitting by adopting a convex hull algorithm and an arc adjacency matrix ellipse detection algorithm; two-dimensional centroid positioning and three-dimensional point cloud analysis technologies are fused, and bimodal calculation in the three-dimensional fruit axis direction is realized in combination with a fruit stem and calyx key point detection result. According to the method, the precision problem of apple posture recognition in a complex natural environment is effectively solved, the anti-interference capability is remarkably improved through the three-dimensional point cloud denoising and feature value analysis technology, and reliable technical support is provided for automatic picking and growth monitoring.

The present invention relates to the field of nondestructive testing technology and provides a composite material damage imaging method based on the spatiotemporal cross-correlation of ultrasonic guided wave wavefields. The method comprises the following steps: S1, acquiring a wavefield signal of the plate being tested; S2, performing narrowband signal extraction on the wavefield signal; S3, filtering the wavefield signal after narrowband signal extraction; S4, shifting the wavefield signal to obtain wavefield signal cross-correlation features; and S5, using a convex hull algorithm to obtain damage imaging results. This method innovatively extracts the difference coefficient between wavefield signals as a basis for damage location, providing a new approach to feature extraction algorithms based on the time-frequency domain. This method enables automated identification of composite material delamination damage and is sensitive to both surface and internal damage.

The invention provides an automatic detection method and system based on a GNSS receiver hardware module, and relates to the technical field of electronic equipment testing, and the method comprises the steps: carrying out the analysis of a preprocessed signal data set, extracting a signal feature boundary based on a convex hull algorithm, and carrying out the detection of the signal feature boundary; calculating key performance indexes such as signal acquisition time, tracking precision, bit error rate and receiving sensitivity; comparing the key performance index with a preset performance threshold value, automatically judging whether the hardware module of the GNSS receiver is qualified or not according to a comparison result, and generating a performance judgment result; and automatically generating a structured detection report based on a performance judgment result, and outputting the report to a specified terminal or a storage position. According to the invention, by simulating GNSS signal excitation, response signal acquisition, a signal processing algorithm and an automatic performance judgment criterion, a scheme for efficiently and fully automatically detecting various key performance indexes of the hardware module of the GNSS receiver is realized.

The invention discloses a tunnel back break analysis method based on a dynamic center line and an incremental convex hull, and belongs to the technical field of tunnel engineering detection. According to the system, the problems that a traditional detection method is low in efficiency and large in error are solved by fusing dynamic slice projection, robust registration and intelligent contour generation technologies. The method specifically comprises the following steps: carrying out configurable downsampling (the density is reduced to 0.2%-0.5% at an interval of 50-1000 points) on an original point cloud, constructing a tunnel three-dimensional model (the side length of a grid is less than or equal to 0.1 m) by adopting a dynamic center line extraction algorithm, and carrying out ICP (Inductively Coupled Plasma) registration (error lt; the method comprises the following steps of: synchronously extracting a design / actual measurement section contour by using a combined slicing technology, generating an actual measurement polygon based on an incremental convex hull algorithm (a filtering threshold value is 20 points), and calculating an over-break volume (an error is less than or equal to 1.5%) by using a spatial Boolean operation. A dynamic center line extraction and incremental convex hull algorithm is innovatively introduced, automatic generation of a color gradation analysis atlas (over-excavation RGB (255, 69, 0) and under-excavation RGB (30, 144, 255)) is supported, and full-process closed-loop processing is realized.

The invention discloses a method, device and equipment for reconstructing an assembled grain three-dimensional model, and relates to the technical field of three-dimensional model construction.According to the method, end face measurement data is used for constructing a grain digital model, and aiming at the problems that an existing point cloud reconstruction technology depends on large-space detection (shooting) equipment and is lack of reconstruction model precision, an end face shooting mode is adopted, and the reconstruction precision is improved. And integrating optimized data into new point cloud data by using a radius outlier removal (ROR) algorithm, a convex hull algorithm and a least square method according to the obtained point cloud initial data, and finally reconstructing a high-precision three-dimensional geometric model. According to the method, the precision of the point cloud reconstruction model is improved, and the applicable scene is expanded.

The invention provides an IGBT (Insulated Gate Bipolar Translator) turn-off time measuring method, system, equipment and medium, and belongs to the field of power semiconductor device testing technology and state monitoring, and the method comprises the following steps: firstly, selecting 20% Vdc and 80% Vdc as turn-off initial measuring points, secondly, carrying out first error correction by adopting a linear estimation method, and finally, carrying out second error correction by adopting a linear estimation method; performing linear fitting on the voltage difference Vce data through a least square method to obtain initial turn-off time; and finally, aiming at systematic deviation caused by inherent concavity and convexity characteristics of the voltage curve, performing secondary error correction by adopting a convex hull algorithm, acquiring a lower envelope line of the ic-tOFF relation curve through an Andriw algorithm, and identifying and eliminating the problem of Overestimation of the turn-off time based on a convex hull accurate point set. According to the invention, accurate measurement of the turn-off time of the IGBT under the condition of low sampling rate is realized, the hardware cost is significantly reduced, and a reliable technical basis is provided for IGBT state monitoring and life prediction.

The invention provides a seed detection method, system and device based on a multi-modal intelligent algorithm. The method comprises the following steps: acquiring a raster image of a to-be-detected seed; rGB-D modal information of the raster image is collected; cutting an edge part in the raster image, and matching the cut image with the RGB-D modal information; performing target detection on the image by adopting a detection algorithm to determine a detection area of the seed in the image; establishing three-dimensional point cloud data according to the RGB-D modal information of the pixel points in the detection area; segmenting the three-dimensional point cloud data into point cloud data corresponding to each seed by adopting an adaptive density clustering algorithm; calculating volume data of each seed by using a point cloud convex hull algorithm according to the point cloud data; and clustering all volume data into real grain data and shriveled grain data by adopting a K-means clustering algorithm. According to the method disclosed by the invention, nondestructive seed test detection of the seeds is realized by adopting a multi-modal detection technology, and detection with higher precision is realized.

The invention relates to the technical field of fingertip touch, and discloses a fingertip extraction touch method and system based on a convex hull algorithm, and the method comprises the steps: collecting an original hand image, and extracting a hand layered sparse point set; performing feature space mapping on the hand layered sparse point set to obtain a hand feature enhanced point set; generating a hand contour convex hull according to the hand feature enhancement point set; performing point angle analysis on the hand contour convex hull to obtain a hand fingertip candidate point set; performing inter-finger relation constraint analysis on the hand fingertip candidate point set to obtain a hand fingertip target point set; and executing critical angular velocity touch control judgment on the hand fingertip target point set to generate a touch control trigger signal. According to the method, the integrity and continuity of a convex hull result are ensured, the problem of incoherence caused by region segmentation is avoided, and the stability in a rapid gesture conversion process is enhanced.

The invention relates to the technical field of froth flotation, in particular to a concentrate froth image recognition and concentrate grade qualification rate prediction method, which is characterized in that a block adaptive threshold segmentation method is adopted to distinguish image foreground and background information, and an image expansion algorithm is adopted to obtain froth contour information; adopting a convex hull algorithm to process vector information of all concentrate foam contours; and according to a set particle threshold value, outputting and calculating the current concentrate grade and an image recognition result to predict the concentrate grade qualification rate. The method has the beneficial effects that in the aspect of feature value extraction of the foam image, the problem of uneven illumination caused by factors such as time and angle is solved by calculating the gradient magnitude and direction of the image, the edge can be accurately identified, the system performs block processing on the foam image, and each sub-module automatically sets an optimal threshold value according to the pixel information of the image, so that the accuracy of feature value extraction is improved. Accurate threshold segmentation of the foreground and background images of the image is realized, and the accuracy and robustness of foam recognition are improved.

The invention discloses a lightweight real-time mapping system based on cloud edge collaboration and a VLA-HDMap high-frequency interaction method, and aims to solve the problems of slow updating, high cost and low safety of HDMap. The model has the capabilities of real-time environment understanding, semantic analysis and end-to-end decision instruction output. The vehicle end can download the cloud HMap in real time, and also participates in crowdsourcing data acquisition and uploading. According to the local lightweight mapping method provided by the invention, a real-time circulation mechanism of'acquisition-application-correction 'is used, so that the HDMap is highly consistent with the actual environment. A cloud edge collaborative hierarchical parallel incremental update global HDMap is also provided, the essence is a time-space intersection multi-scale analysis model of minute-level traffic flow prediction and day / week-level map differentiation detection, a high-risk region of traffic flow peak + map change can be efficiently identified, and a convex hull algorithm is used to obtain a region, which needs to be locally updated, of the whole HDMap. According to the invention, a new-generation intelligent traffic system can be constructed, and the safety application of group intelligence, symbiotic economy and large-scale automatic driving can be promoted.

The invention relates to an optimal scheduling method for port system electricity-hydrogen energy resource allocation, and belongs to the field of resource optimal scheduling. The method comprises the following steps: establishing a mapping relation between a logistics operation organization mode and an energy resource demand, and forming a unified collaborative information model; based on the collaborative information model, a configuration decision evaluation model considering electrochemical energy storage and hydrogen energy resources is constructed and used for generating evaluation indexes corresponding to a resource configuration scheme under the condition that the resource configuration scheme and a logistics operation organization scheme are given; constructing a three-dimensional decision-making space taking the operation cost, the carbon emission and the average ship in-port time as dimensions, constructing an observation grid on a preset discrete value of a corresponding index, solving a boundary state point set point by point, and constructing an envelope surface of the three-dimensional decision-making space by using a three-dimensional convex hull algorithm; and screening a configuration scheme based on decision boundary characteristics of the three-dimensional decision space. According to the invention, the applicability of an electricity-hydrogen energy resource configuration result in a complex operation scene can be improved.

The invention provides a live broadcast automobile marketing flow fluctuation prediction method and system, and relates to the technical field of cross-domain fusion, and the method comprises the steps: synchronously collecting data in a live broadcast platform and cross-domain extension data, and generating a platform data set and a cross-domain data set; based on the cross-domain data set, constructing an association space of the social hot events and the vehicle type attributes through a mirror image algorithm, and when the event feature intensity of the association space exceeds a preset threshold value, generating an event influence feature value; based on the event influence characteristic value and the platform data set, generating a basic flow prediction curve according to a fixed period and selecting a key point set; and processing the key point set through a convex hull algorithm to generate a convex polygon region. According to the invention, through constructing a full-link closed-loop design of data acquisition-event association-traffic prediction-parameter optimization-promotion triggering-verbal skill adjustment, accurate control of live broadcast automobile marketing traffic fluctuation and improvement of the marketing effect are realized.

The invention relates to a diffracted guided wave-based metal cylinder crack detection method, which comprises the following steps of: axially arranging a sensor array on a metal cylinder in an array manner, and collecting signals of all sensing paths; the sensing path is a connecting line between each excitation sensor and each receiving sensor; preprocessing each received path signal, judging a direct wave and a diffracted wave, and constructing a damage index of each sensing path on the basis of the amplitude difference between the diffracted wave and the direct wave; according to the signal and the damage index of each sensing path, drawing a total damage path diagram in the whole detection range, counting intersection points of each damage path and calculating the damage index of the corresponding intersection point; an improved clustering algorithm is utilized to obtain effective damage intersection points and a weighted centroid, and a damage area of the metal cylinder is delimited based on the damage intersection points around the weighted centroid through a convex hull algorithm. Compared with the prior art, the method has the advantages that dependence on health reference data can be eliminated, the calculation complexity is effectively reduced, and the detection efficiency is improved.

The invention relates to the technical field of image recognition, in particular to a face recognition method, device and equipment applied to an enterprise management system, and the method comprises the steps: obtaining a to-be-detected face image; obtaining non-skin pixel points based on the color features of the pixel points; partitioning the image, and combining a convex hull algorithm to obtain a human face shielding degree; performing connected domain extraction on non-skin pixel points in all convex hulls in the to-be-detected face grayscale image; according to the gradient features and the color features of the edge pixel points of each connected domain in each convex hull, obtaining the suspected degree of the five sense organs of the human face; according to the distribution of the face five-sense-organ suspected degree of the connected domain where the pixel points in the pixel blocks are located, obtaining the information carrying degree; and combining the LBP coding histograms of the pixel blocks of the to-be-detected face grayscale image and the standard face grayscale image to obtain a face recognition result. The method aims at extracting the feature information amount borne by different areas of the face, and the accuracy of face recognition based on the LBPH algorithm is improved.

The invention discloses an orbit foreign matter detection method and system, and relates to the technical field of orbit detection.The method comprises the steps that an unknown foreign matter model is built through Gaussian distribution and a convex hull algorithm, and a known foreign matter model is built in combination with prior data; extracting a flat area of the ballast bed, projecting the foreign body model into a depth map and generating a shadow, and superposing to obtain a synthetic image with a mark; training a segmentation network by using the synthetic image and a real image, and obtaining a potential foreign matter mask through thermodynamic diagram binaryzation, connected domain analysis and size screening during reasoning; and inherent facility false alarms are eliminated through a secondary classification network, a real foreign matter detection result is finally output, alarm is triggered according to a detected threshold value, and efficient and accurate detection is realized. According to the method, on the premise that real foreign matter samples are scarce and ballast bed scenes are complex, efficient, accurate and real-time ballastless track foreign matter detection is achieved, meanwhile, the false alarm rate is reduced, and the generalization ability of the model to unknown foreign matter is improved.

The invention discloses a mountain torrent debris flow geometric parameter and surface flow field real-time calculation method and system, and belongs to the technical field of geological disaster monitoring, and the method comprises the following three core steps: S1, precisely calculating the geometric parameters of a riverway, determining the main axis of the riverway through principal component analysis, and calculating the width through a multi-thread parallel convex hull algorithm; efficient depth inversion is realized through a KDTree spatial index, and an Alpha shape is utilized to extract a central area form so as to estimate flow; s2, dynamically analyzing a surface flow field, constructing a grid model by using Delaunay triangulation, rendering and enhancing micro-topographic features based on gradient topographic shadow, and extracting a surface flow velocity field from continuous topographic shadow by using a PIV technology; and S3, data stream processing and transmission architecture: constructing a directed acyclic graph topological structure and a dual-channel data transmission mechanism. According to the invention, the problems of real-time performance and accuracy of debris flow monitoring in a complex mountain area environment are solved, the system delay is less than 300 milliseconds, and reliable technical support can be provided for early warning of mountain torrents and debris flow disasters.

The invention discloses a power network dynamic security protection system and method based on multi-source data fusion, and relates to the technical field of power systems. According to the method, the original heterogeneous data set is generated by comprehensively collecting the multi-dimensional data of the power network, and the problems of data format difference and time asynchronization are solved through protocol conversion and timestamp calibration; the line bearing capacity is dynamically calculated by fusing multiple types of data, the equipment risk level is generated, and the abnormal current feature extraction precision is enhanced by combining a mirror image algorithm, so that the security situation assessment is more accurate and comprehensive; abnormal current fluctuation is used as a trigger point, network malicious features and environment abnormity records in the same area are associated through a convex hull algorithm, threat types are comprehensively judged, and a target is clarified for protection execution. Threatened areas are rapidly isolated, alarms are pushed, a standby power supply is started, important user load distribution is adjusted in combination with a convex hull optimization algorithm, the fault influence range is reduced, key power supply is guaranteed, and the emergency processing capacity and the continuous power supply reliability are improved.

The invention discloses a monitoring data analysis system and method applied to mutton sheep group production evaluation, and relates to the technical field of big data analysis, and the method comprises the steps: building a material pile error analysis set, processing point cloud data in combination with a convex hull algorithm, avoiding an irregular material pile volume calculation error, and introducing temperature and humidity sensitivity coefficients. The influence of environmental factors on natural settlement is quantified, and the problem of volume misjudgment caused by natural settlement in a traditional method is solved. According to the method, a feed pile density entropy mapping set inside and outside a trough is established, the physical density of the feed pile and the image gray entropy are associated, abnormal points are removed, the feed pile density is quickly and reversely deduced through image features, and an efficient non-contact feed throwing loss monitoring means is provided. Meanwhile, multi-source data and a dynamic correction mechanism are fused, a natural settlement quantitative model and a histogram normalization cross-correlation algorithm are combined, the residual material pile weight is corrected through the volume change rate, factors in multiple aspects are comprehensively considered, interference is eliminated, and the effective ingestion weight is accurately calculated.

The invention provides a single tree crown volume accurate calculation method based on a double-constraint convex hull algorithm and a filtering triangulation network algorithm, and belongs to the field of crown algorithms, the algorithm is composed of an optimized convex hull algorithm and a filtering triangulation network algorithm, catalpa bungei is taken as a main research object, 100 single trees are manually separated, a crown point cloud extracted by an optimal elevation threshold is calculated by using DB, and the crown point cloud is calculated by using a DB algorithm. And comparing and discussing with other conventional algorithms. Compared with other algorithms, the DB algorithm has the advantages that gaps caused by convex hulls are greatly reduced, the problem of a complex crown shape with multiple branches is solved, and noise immunity is enhanced. In addition to errors caused by shape selection in a geometric algorithm, compared with other algorithms, correlation coefficients all exceed 0.9. A convex hull edge length threshold (CHL) and a triangulation network length threshold (TL) are keys for controlling algorithm precision, CHL balances efficiency and precision of convex hull boundaries from 0.04 m to 0.1 m, and TL controls automatic separation precision of multi-branch point clouds from 0.4 m to 1 m.

The invention belongs to the technical field of tunnel engineering digital construction, and particularly relates to an unsupervised learning drilling and blasting method tunnel point cloud feature extraction and back break detection method. The method comprises the following steps: firstly, acquiring initial point cloud data of tunnel excavation by adopting three-dimensional laser scanning, and preprocessing by adopting point cloud thinning and registration; then, a convex hull algorithm is used for calculating an actual excavation axis, excavation sections with the thickness of 0.1 m are cut out according to the fixed interval of 10 m, a K-means clustering algorithm is used for conducting noise reduction processing on point cloud slices, and convex hull vertexes are screened to accurately extract actual excavation contour lines; and finally, the back break amount of each convex hull is calculated, and real-time quantitative analysis of the back break condition of the tunnel is achieved. According to the method, by fusing three-dimensional laser scanning, unsupervised learning and a convex hull algorithm, crossing of tunnel back break detection from discrete, lagging and manual detection to continuous, real-time and intelligent detection is achieved, and efficient, accurate and reliable digital technical support is provided for drilling and blasting method tunnel construction.

The invention provides a method and a system for determining operation nodes in an organoid culture process, and relates to the technical field of data processing, and the method comprises the following steps: carrying out interval division on a state track curved surface to obtain a plurality of physiological state intervals; performing convex hull algorithm calculation on the data point set in each physiological state interval, obtaining distribution boundary characteristics of the data points by constructing a minimum convex polygon, and obtaining morphological dynamics indexes based on the distribution boundary characteristics and change gradient calculation; performing normalization calibration processing on the monitoring data according to the morphodynamic indexes to obtain a calibrated growth state evaluation value; comparing the calibrated growth state evaluation value with a preset operation threshold value to obtain a comparison result; and determining an operation node of the organoid growth state based on a comparison result. According to the method, the operation node of the organoid culture can be accurately determined, and the controllability of the culture process and the stability of the growth state of the organoid are effectively improved.

The invention discloses a convex hull algorithm optimization and applicability analysis method based on rotation contraction. The method comprises the following steps: step 1, processing a point set by a rotation contraction preprocessing strategy; and step 2, convex hull calculation. According to the method, the filtering operation is executed, a large number of non-convex hull points in the point set are deleted, the convex hull is calculated based on the classical convex hull algorithm, the convex hull algorithm optimization and applicability analysis method based on rotation contraction is constructed, the computer memory can be saved when the convex hull is calculated based on the method, meanwhile, the convex hull calculation time can be shortened, and the calculation efficiency is improved. And technical support is provided for the fields of computational geometry, computer graphics, computer vision and the like.

This invention provides an automated testing method and system for GNSS receiver hardware modules, relating to the field of electronic equipment testing technology. The method includes: parsing a preprocessed signal dataset and extracting signal feature boundaries based on a convex hull algorithm, calculating key performance indicators such as signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity; comparing these key performance indicators with preset performance thresholds, automatically determining whether the GNSS receiver hardware module is qualified based on the comparison results, and generating a performance judgment result; and automatically generating a structured test report based on the performance judgment result, and outputting the report to a designated terminal or storage location. This invention achieves an efficient and fully automated solution for testing various key performance indicators of GNSS receiver hardware modules by simulating GNSS signal excitation, response signal acquisition, signal processing algorithms, and automated performance judgment criteria.

The present invention provides a method, system, terminal, and medium for aggregating multi-target REDD project areas, specifically relating to the field of forest carbon sequestration technology. This solution constructs a multidimensional weighted feature set for each coordinate point based on the collected coordinate points within several target REDD project areas and the geographic feature data of the target layer at each coordinate point, as well as preset spatial coordinate aggregation weights and preset geographic feature aggregation weights. A clustering algorithm is used to cluster all the multidimensional weighted feature sets to obtain grid point sets corresponding to the multidimensional weighted feature sets. A convex hull algorithm is used to calculate the convex hull of the coordinate points in each grid point set to obtain the convex hull set of the grid point sets. Based on the target REDD project areas corresponding to all convex hull sets, an aggregated region for all target REDD project areas is obtained. This solution can effectively improve the accuracy of REDD project area boundary identification, thereby facilitating the comprehensive management and assessment of dispersed areas.

The invention discloses a new energy high-permeability power distribution network power balance method, and the method comprises the steps: firstly constructing a seasonal and moment-based wind and light confidence output probability model based on historical data; secondly, by utilizing the model, selecting new energy output values under corresponding confidence coefficients for different typical day scenes to carry out power balance analysis so as to determine a power grid construction scale; then, a fast convex hull algorithm is adopted, decoupling and enumeration regrouping are carried out on the time sequence data of the load and the new energy output, and an extreme operation mode curve representing the power shortage risk and the consumption challenge is systematically extracted; and finally, by taking the extreme curve as a security boundary, comprehensively considering the coordinated regulation capability of the source, the network, the load and the storage, and performing security verification on the power grid in the planning year. According to the method, confidence modeling and extreme scene extraction are combined, the defect that a traditional method is too conservative or insufficient in scene coverage is overcome, safe and stable operation of a power grid can be guaranteed, and meanwhile the economical efficiency of power grid investment is remarkably improved.

The invention discloses a method for tracing pollutants in a lake, which comprises the following steps of: (1) gridding a satellite map of the lake, and collecting pollutant characteristic signal data of a suspected pollution source and a lake background point water sample; (2) supplementing the suspected pollution source by using principal component analysis, a convex hull algorithm and clustering analysis according to a topological structure of the background point and the suspected pollution source; (3) labeling the suspected pollution source data set, establishing a random forest model, and performing classification training on the random forest model by using the suspected pollution source data set; (4) calculating the pollutant characteristic signal intensity of the whole lake area by using diffusion simulation inversion; and (5) normalizing the pollutant characteristic signal intensity of each coordinate position of the whole lake area, inputting the normalized pollutant characteristic signal intensity into the trained random forest model, calculating the influence ratio of each suspected pollution source on each coordinate position, and drawing a spatial distribution diagram of the influence ratio of each pollution source. The method provided by the invention is high in accuracy and small in required manpower and computing power.

The present application relates to photovoltaic planning technical field, especially to a kind of solar radiation resource evaluation method for photovoltaic power station.The method is first according to installed capacity target to determine ideal state laying area, and combined with the building coverage of target area and shading loss is modified, and delimitation redundant target area.Subsequently, candidate point is screened in area, using the energy efficiency evaluation index of expected radiation resource and three-dimensional topological grid line cost, point is divided into different spatial clustering cluster by clustering algorithm.Aiming at each cluster, the minimum circumscribed polygon area is fitted using the convex hull algorithm, and the site selection area set is formed.Further, the present application simulates dynamic shadow by digital elevation model and ray tracing algorithm, and calculates the actual radiation resource amount of each area;if the total resource amount does not meet the target, a multi-objective function is introduced for Pareto optimization iteration, and a matching solution is selected from the optimal solution set and the polygon boundary is adaptively adjusted until the demand is met.