47 results about "Grid analysis" patented technology

The invention provides an operation optimization method and system based on electric power data conjoint analysis, and the method comprises the steps: obtaining synchronous sampling data and power grid topological parameters of a wide-area electric power system; performing equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification on the synchronous sampling data and the power grid topological parameters by using a conjoint analysis engine, and generating a power grid control strategy; and carrying out operation optimization on the power equipment in the wide-area power system based on the power grid control strategy. According to the method, conjoint analysis is carried out on equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, the power grid analysis depth and accuracy can be improved, the high-frequency harmonic analysis requirement can be met through broadband resonance traceability, and the method is suitable for large-scale popularization and application. Intelligent analysis can be realized for different application scenes, the waveform analysis capability and the frequency response range are improved, and the analysis effect is improved.

The invention provides an ultra-thin coal seam collapse early warning system based on multi-field dynamic analysis and an optimization method thereof, and aims to carry out fusion and deep analysis on data by monitoring a plurality of physical data such as a temperature field, a confining pressure field and a gas field in a coal seam in real time and combining a parallel multi-grid analysis algorithm and an LSTM time sequence analysis technology so as to realize early warning of the collapse of an ultra-thin coal seam. Therefore, early warning of the ultra-thin coal seam collapse disaster is realized, and the safety of coal mine production is ensured. The system is composed of a monitoring sensor network, a data acquisition module, a data fusion and analysis module, an early warning decision module and a display and alarm module, can dynamically predict the dangerous state of a coal seam according to multi-field coupling data, and sends out an early warning signal in time when an abnormality is found. By accurately arranging the sensors and adopting an efficient multi-grid algorithm and a powerful LSTM analysis model, the system can realize intelligent monitoring and accurate early warning of the safety state of a coal mining area in a complex environment, and the safety and intelligent level of coal mine production are greatly improved.

The invention provides an autonomous exploration method and device for an indoor mobile robot. The method comprises the following steps: performing pose estimation on the indoor mobile robot based on point cloud data and sensor data fusion; processing the point cloud data by adopting a method of combining fan-shaped projection and binary Bayesian filtering, carrying out environment modeling and constructing a lightweight grid map; according to the environment information on the lightweight grid map, utilizing a ray edge grid analysis method, a Bayesian inference edge grid intersection segmentation method, a leading edge region segmentation method and a viewpoint sampling and evaluation method to detect a leading edge region of the explored region and segmenting the leading edge region into a plurality of sub-regions; generating an optimal viewpoint of each sub-region; exploring an unknown environment by using the optimal viewpoint and adopting a hierarchical exploration strategy combining global exploration and local exploration to obtain a planned path; and according to the planned path, generating a navigation trajectory by adopting a method of combining multi-constraint trajectory optimization and nonlinear model predictive control.

The invention belongs to the technical field of gas pipelines, and particularly relates to a gas engineering construction quality evaluation system based on big data, which comprises the following steps of: firstly, constructing a defect feature tensor, introducing a space-time grid analysis technology to analyze defect disposal aging interval data and location defect reproduction frequency data, and constructing a defect feature tensor on the basis of a preset quality-efficiency reliability quantification rule; defect features are converted into quantifiable quality-effect scores, then deep fusion of multi-source heterogeneous data is achieved by establishing a space-time incidence matrix of welding defects, welding process parameters and environment monitoring parameters, and on this basis, a fuzzy correlation analysis technology and a preset process compliance quantification rule are adopted to achieve quality-effect evaluation of the welding defects. Complex process parameters and environmental conditions are converted into quantifiable process scores, the process scores and quality-effect scores are integrated to generate a gas pipeline welding quality comprehensive evaluation value of a target contractor, and a quantifiable intelligent decision support system is provided for gas pipeline construction quality management and control of the contractor.

According to the automatic production line digital model hole filling method and device provided by the embodiment of the invention, the to-be-processed area is accurately identified through multi-level grid analysis and curvature field calculation. A patch generation mechanism based on density clustering and a neural network is innovatively designed, and intelligent filling optimization is realized in combination with local geometric features. According to the system, non-uniform rational basis spline surface reconstruction and a Laplacian grid smoothing algorithm are adopted, continuity and fairness of a filling area are ensured, and the system is closely combined with motion control and path planning of an automatic production line. According to the method, the limitation of traditional model repair is broken through, and an efficient and reliable solution is provided for integrity reconstruction of the industrial digital model.

The invention discloses an electrified railway contact line icing influence analysis method, which comprises the following steps of: obtaining key parameters such as a collision coefficient and a freezing coefficient of a water drop and a contact line in an icing calculation process by analyzing a line surrounding flow field of an overhead line system and researching heat balance of the surface of the contact line; and meanwhile, carrying out grid analysis on environmental parameters and icing boundaries so as to establish a time-varying meteorological numerical model of the contact line. By means of a contact line icing numerical model, the influence of the temperature (T), the liquid water content (LWC), the median volume diameter (MVD) and the wind speed (Vp) on contact line icing evolution is analyzed through a control variable method. Therefore, an important basis is provided for contact line icing prediction, ice prevention and ice removal in the future.

The invention relates to the technical field of tourism information service, in particular to a route guiding system for scenic spots based on urban and rural cultural tourism big data, which is characterized in that gate traffic volume and connection vehicle positioning data are acquired, tourist tracks are mapped to grid units, high-traffic areas are screened, and capacity and route integrating degree is evaluated. And sorting the culture similarity, the arrival interval and the element density, dynamically adjusting a gradient value, and combining an explanation point and scheduling data to generate a scheme. According to the method, the gate traffic volume, connection vehicle positioning and tourist trajectory data are integrated, a dynamic thermodynamic diagram is generated through gridding analysis, the passenger flow gathering area is recognized, and the response speed is increased. And dynamically matching the subject weight based on the traffic volume and the residence density difference value, and generating a diversion list of linkage of the deviation area and the resources. And constructing a route cluster adaptive to azimuth angle deviation by combining the dispatching capacity of the connection vehicle with the culture attribute similarity, and optimizing the transport capacity and culture experience collaboration.

The invention provides an analysis alarm video monitoring platform for wind power generation equipment, and relates to the technical field of video monitoring, the video monitoring platform comprises a real-time picture acquisition module, a historical picture acquisition module, a gridding division module, an analysis module and an alarm generation and management module, the video monitoring platform is also provided with a three-level architecture, the three-level architecture comprises a group headquarter unit, a province unit and a basic level unit. According to the method, after the alarm is triggered through grid analysis, the current / historical picture data is automatically associated, and the alarm event with the grade is generated by means of the event playback function, so that the false alarm caused by environmental interference is greatly reduced, and the alarm accuracy is improved. And a current picture transmitted by the video acquisition module is obtained through a real-time preview mode, and is compared with a specified historical moment picture called from the hierarchical storage system through the intelligent index, and the historical picture is used as a contrast to judge whether an abnormality exists or not and whether alarm processing needs to be carried out or not.

The invention provides a ship pier head structure strength evaluation method, which aims at the problem that a ship bow structure is easy to damage or deform under a ship pier falling working condition, obtains an accurate rigidity coefficient of a docking block through actual measurement and conversion, and establishes a complete real ship finite element model in an evaluation range to reflect accurate mass distribution and rigidity. Coarse grid and fine grid grading evaluation is adopted, the efficiency is improved while the calculation precision is guaranteed, coarse grid analysis is used for obtaining docking block counter-force, fine grid analysis can consider actual counter-force distribution in contact with a docking block, and the yield strength and buckling strength evaluation safety coefficient of the ship bow structure is provided; the damage or deformation risk of the ship docking pier falling structure can be systematically and accurately evaluated, the ship pier falling optimization design is guided, and the ship construction period can be shortened.

This application discloses a method, system, and device for identifying target geological structures in coal seams. The target geological structures include folds, faults, lower local thickening zones, and upper local thickening zones. The method acquires full measured data of cross-layer boreholes in the target longwall face, along with the strike, dip, and true dip parameters of the coal seam. Preprocessing yields the three-dimensional coordinates of the borehole entry point, the three-dimensional coordinates of the exit point, and the true thickness of the coal seam. A square nine-grid analysis window is constructed with the strike and dip of the coal seam as the directional reference. Target cross-layer boreholes with stable coal seam thickness are selected based on the deviation of the true thickness of the coal seam. A spatial plane equation of the coal seam is established to calculate the theoretical entry point elevation of the target cross-layer borehole, generating regional target geological structure identification results. A sliding window traverses the entire longwall face, outputting the overall regional target geological structure identification results. This method can stably identify hidden small target geological structures and significantly improves computational efficiency compared to traditional methods. It can be widely applied to advanced geological exploration scenarios in coal mine longwall faces.

The embodiment of the invention provides a power distribution network load adjustment method, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring node data of a plurality of power grid nodes in the power distribution network; wherein the node data comprises current, voltage and position of each node and a power transmission relation between different power grid nodes; constructing a power grid static topological structure based on the node data, and determining power grid analysis parameters; the power grid analysis parameters comprise reactance, resistance and admittance among power grid nodes; determining the equivalent static load of the plurality of power grid nodes based on the dynamic response relationship between the power of the plurality of power grid nodes and the voltage fluctuation and the power grid transmission parameters; adjusting the loads of the plurality of power grid nodes based on the objective function, and determining the load of each power grid node according to an adjustment result; wherein the objective function represents that the sum of the power transmission efficiency and the dynamic load adjustment cost is minimized. The method is used for improving the load adjustment precision of the power distribution network.

The application relates to a WebGL-based three-dimensional local terrain protrusion weakening method, and belongs to the technical field of computer graphics visualization, which comprises terrain grid loading, loading terrain data in the form of a grid into a scene in a WebGL rendering engine, storing information in the form of a.terrain format binary file, then performing grid analysis, loading block terrain data according to a terrain level of detail strategy, analyzing terrain data, loading and analyzing downloaded binary file data according to terrain grid levels and row and column numbers, compressing the data by using a zig-zag coding method, then performing vertex height dynamic adjustment, and finally performing terrain triangular net reconstruction. The WebGL-based three-dimensional local terrain protrusion weakening method can weaken terrain protrusion problems by modifying the vertex elevation of a terrain data-free area, does not cause distortion of local high-precision terrain and models in a scene, can weaken terrain cliff problems existing in three-dimensional local terrain protrusions, and does not deform the terrain and models in the scene.

The invention is suitable for the technical field of Internet, and relates to a battery car battery replacement cabinet site selection optimization method, a medium and a terminal, the process is simple, the operation is convenient, province and city latitude and longitude administrative division, DEM data, population data and POI data are fused, comprehensive site selection is performed by integrating multiple factors, and site selection is more scientific and reasonable; dynamic grid division is carried out based on Mercator conversion, accurate division of the region is realized, and a good basis is provided for subsequent analysis; internal and external indexes of the grid are constructed, and characteristics and actual requirements of different regions are fully considered; a K-means clustering method is used for constructing a negative sample, so that the reliability and the accuracy of the model are improved; through the integration of grid analysis and Logistic and RF regression, the transformation of the site selection of the power conversion cabinet from experience driving to data driving is realized. According to the method, the problems of low efficiency, high cost and unreasonable coverage caused by the fact that a traditional battery car battery replacement cabinet site selection method depends on artificial experience are solved.

This invention relates to the field of energy control, specifically to a multi-energy collaborative intelligent microgrid control system. It includes a management center communicatively connected to a grid acquisition module, a grid analysis module, and a grid control module. The management center acquires historical energy data and historical electricity consumption data, and, combining these data, establishes an energy grid model. It then analyzes the energy grid to obtain energy region data. Based on the energy observation cycle and energy region data, it establishes a two-dimensional grid coordinate system and constructs a time-series prediction model to obtain predicted grid data. Using the predicted grid data, it constructs a predicted grid matrix and uses this matrix to regulate energy in the power region, obtaining pre-regulation strategies and optimal energy distribution points. Finally, it analyzes the pre-regulation strategies based on real-time electricity consumption and real-time power generation to obtain actual regulation strategies. This invention enhances the stability and flexibility of the microgrid.

The application provides an analysis alarm video monitoring platform for wind power generation equipment, relates to the technical field of video monitoring, and comprises a real-time picture acquisition module, a historical picture acquisition module, a grid division module, an analysis module and an alarm generation and management module. The video monitoring platform is also built with a three-level architecture, which comprises a group headquarters unit, a provincial unit and a basic unit. After triggering the alarm through grid analysis, the current / historical picture data is automatically associated, the alarm event with levels is generated by means of the event playback function, the false alarm caused by environmental interference is greatly reduced, and the alarm accuracy is improved. The current picture transmitted by the video acquisition module is acquired through the real-time preview mode, and is compared with the specified historical time picture retrieved from the hierarchical storage system through the intelligent index. The historical picture is used as a reference to determine whether there is an abnormality and whether alarm processing is needed.

The invention discloses a dust multi-dimensional cloud monitoring and intelligent control system, which belongs to the technical field of environment monitoring and comprises scanning equipment, fixed monitoring equipment and dust suppression equipment. The system generates a quantized dust cloud picture by fusing the scanning distribution map and the real-time concentration value, and performs gridding analysis; performing reverse traceability on the standard exceeding points by using a reverse diffusion model, and determining a main pollution source grid; and automatically controlling the dust suppression equipment of the corresponding grid to start based on comparison between the real-time index and the threshold value. According to the invention, a monitoring blind area is eliminated, and global quantitative monitoring, accurate traceability and intelligent closed-loop treatment of dust are realized.

The present invention discloses a system for monitoring a palm tree and an oil palm fruitlet in a plantation in a lightweight and efficient manner and a method thereof. The system comprises a data acquisition module (100), a tree detection module (200), a fruitlet detection module (300), and an environment representation module (400). The data acquisition module (100) comprises a remote sensing device (101) and an image-capturing device (102) mountable to a mobility platform (103) for generating an occupancy grid map associated with the palm tree in the plantation and an image associated with the oil palm fruitlet scattered within a vicinity of the palm tree, respectively. The tree detection module (200) generates a submap from the occupancy grid map to detect the palm tree therein and determine a tree position of the palm tree detected thereof, which is transformed to a global tree position on a semantic or global map. The fruitlet detection module (300) detects the oil palm fruitlet from the image, a fruitlet position, and a quantity of oil palm fruitlets using a grid analysis and transforms the fruitlet position to a global fruitlet position. The environment representation module (400) compares the tree position against a tree tracking list using a chain-based tree-tracking and re-mapping algorithm and assigns a harvest-ready status to the palm tree based on the quantity of oil palm fruitlets detected thereof.

The invention relates to the technical field of site selection of recycling facilities, in particular to a new construction waste industrial park site selection method based on grid analysis. Comprising the following steps: determining a site selection evaluation area, an industrial park processing scale and grid unit parameters; converting the rigidity and flexibility evaluation indexes into a rasterized thematic map layer; building waste generation amount estimation, space distribution and building waste generation distribution map layer generation; performing Boolean overlay analysis on the hard indexes to obtain a primary selection range; the weight of the flexible index is determined by adopting a CRITIC method, and an alternative site selection scheme is obtained after weighted superposition analysis; and constructing a multi-objective optimization model of the newly-built construction waste industrial park, and obtaining a Pareto optimal solution set through an NSGA-II algorithm. According to the method, the generation space distribution of the construction waste and the objectivity of the index weight are comprehensively considered, the scientificity and systematicness of site selection of the construction waste industrial park are improved, and the method has good universality and popularization value.

The invention discloses a radar rainfall deviation correction algorithm based on combination of probability density matching and a climate state ratio method. The radar rainfall deviation correction algorithm comprises collection and preprocessing of ground station observation data, PDF deviation correction of radar rainfall and climate state deviation correction of radar rainfall. According to the invention, effective utilization of rainfall observation data of a ground meteorological station is realized, deviation correction is carried out on a radar networking estimation rainfall product by using a ground station grid analysis field and a ground-radar climate state proportion analysis field, and the deviation between a radar rainfall estimation value and real rainfall is effectively corrected. According to the method, the discontinuity and systematic deviation of space-time distribution of a fusion product are reduced, the adaptability of the fusion product to complex terrains and rapid evolution precipitation scenes is enhanced, collaborative optimization correction of multi-scale and multi-type deviation is realized, and the reliability and service applicability of a radar precipitation product are remarkably improved.

A transmission / distribution system operation coordination-based HVDC operation system is provided, based on the operation of HVDC (High Voltage Direct Current) to expand supply stability, improve power quality, and enhance the economic efficiency of system operation throughout the domestic power system. The HVDC operation system is characterized by comprising: a power system management terminal that generates power system operation information; a plurality of HVDCs that generate HVDC availability information; a power market management server that generates power market operation information; distribution network operation information; a distribution system management computer that generates distribution network generation planning information and real-time distribution network generator operation information using distribution network generator bidding information and transmission system distribution network generation capacity acceptance information among the power market operation information; and an HVDC operation computer that generates HVDC operation planning information and distribution network generation capacity acceptance information based on the distribution network generation planning information, and generates real-time control information for controlling the operation of the HVDC based on the HVDC operation planning information, power system operation information, and distribution network operation information.

The invention discloses a vertical face positioning method and system for a marine wall-climbing robot. The method comprises the steps that S1, the system is initialized, and a global grid is established; S2, the robot is initially calibrated and placed; s3, trajectory planning and loading are carried out; s4, fusing positioning and closed-loop control; s5, deviation judgment and correction; s6, the step S4 and the step S5 are executed repeatedly until all planned tracks and operation tasks are completed; according to the method, the laser grid is projected, a stable and visible global position reference is created on a feature-free vertical face, and the positioning problem that no absolute reference object exists in a large-scale space is solved; absolute position information without accumulated errors is provided through visual grid recognition calculation, long-term drifting of the IMU and the odometer is corrected, and the positioning precision of long-time operation is guaranteed; the wall-climbing robot continuously detects the pose deviation and actively corrects the pose deviation according to the visual grid analysis result in the advancing process, the accumulated error of the IMU is corrected in real time by using the visual observation value, and the robustness and reliability are improved.

The invention provides an oven temperature and time intelligent cooperative control system based on food material moisture dynamic perception, and the system collects multi-dimensional data in a cooking process in real time through a data collection module, carries out the preprocessing and grid analysis of the collected data through a data analysis module, determines the real-time state of a food material, screens out the to-be-processed candidate region of the food material, and carries out the processing of the to-be-processed candidate region of the food material. Analyzing the current cooking stage according to the food material state; the control module outputs control instructions of temperature, time, heating power and hot air circulation strength based on a cooperative control algorithm according to the current cooking stage of the food materials and the candidate area to be processed; and the execution module receives and analyzes the control instruction, and controls an execution mechanism to act according to the analyzed control instruction. Dynamic matching of temperature, time and food material states can be achieved, the problem that a traditional oven is insufficient in intelligence is thoroughly solved, the baking efficiency and the cooking quality are improved, the user operation threshold is lowered, and the oven is particularly suitable for home kitchens, small catering institutions, baking studios and other scenes.

The invention discloses a regional adaptive OI fusion algorithm based on station network density, terrain gradient and radar quality index. The regional adaptive OI fusion algorithm is constructed by using a ground station precipitation grid analysis field, a radar satellite joint background field, a radar quality index analysis field (RQI), a station network density analysis field (SD I) and a terrain gradient analysis field (SLP). According to the method, the precipitation fusion precision of the complex underlying surface area is remarkably improved. The rainfall error space-time structure of different areas (such as mountainous regions, river valleys and coastal zones) can be accurately captured. In a radar signal area shielded by the terrain, the error ratio of an observation field to a background field is automatically improved, and low-quality radar data is prevented from being excessively trusted; in the abrupt slope mountainous area with a sparse station network, the background field error attenuation distance is shortened, and the problem of'false details' caused by false interpolation is effectively inhibited. In a plain area with dense station networks, the attenuation distance is prolonged to keep the natural continuity of the precipitation field, and excessive smoothness is avoided.

The embodiment of the application provides a kind of automated production line digital model hole filling method and device, accurately identify the region to be processed by multilevel grid analysis and curvature field calculation.Innovatively designed the generation mechanism of patch based on density clustering and neural network, combined with local geometric features to realize intelligent filling optimization.The system uses non-uniform rational basis spline surface reconstruction and Laplace grid smoothing algorithm to ensure the continuity and fairness of the filling area, and is closely combined with the motion control and path planning of automated production line.This method breaks through the limitations of traditional model repair and provides an efficient and reliable solution for the integrity reconstruction of industrial digital models.

This application proposes a weight evaluation method based on spatial grid analysis and attention mechanism, which relates to the field of image processing technology, including: obtaining a gridded evidence image and attribute information of the evidence image, wherein the attribute information includes the position information of each grid; determining the grid weight of each grid in the evidence image based on the attribute information of the evidence image; determining the attention weight of each grid in the evidence image based on the similarity between each grid in the evidence image and a reference grid; and determining the comprehensive weight factor of each grid in the evidence image based on the grid weight and attention weight of each grid in the evidence image. Using the weight evaluation method of this application to evaluate the weight of the evidence image, and training the image classification model based on the gridded evidence image and the comprehensive weight factor of each grid can improve the training effect of the image classification model.

The invention relates to a contour prediction method for a femtosecond laser rotary-cut small hole. The femtosecond laser rotary cutting machining method is provided for solving the problems that an existing large-depth-diameter-ratio small hole is difficult to machine, and the machining quality is difficult to control. Femtosecond laser rotary cutting machining is a machining technology for preparing high-precision small holes, a laser beam rotates around an optical axis at a high speed to form a light knife, materials are removed layer by layer by controlling the light knife to move along a spiral path, and the purposes of accurately controlling the hole diameter and the shape, improving the machining efficiency and the like are achieved. According to the method, the distribution of laser spots in the laser ring is analyzed, and the laser flux of the light knife is obtained according to a laser flux line and a grid analysis method. The motion trail of a light cutter during machining is analyzed, the evolution law of hole contour lines under different feeding depths during machining is mastered through an ablation depth calculation method based on an ablation threshold value, a contour prediction model during femtosecond laser rotary cutting machining of the small holes is established, and theoretical support is provided for machining of the large-depth-diameter-ratio small holes of materials difficult to machine.

The invention discloses a tea tree anthracnose diffusion prediction method based on a remote sensing image and lattice point analysis, and the method comprises the following steps: obtaining an unmanned plane multispectral image of a tea tree land parcel, and carrying out the recognition of a soil background region, a healthy tea tree region and an infected region; dividing the tea tree plot into uniform grid spaces, defining the state of each grid point as a healthy grid point or an infected grid point, setting a diffusivity calculation method, and calculating the diffusivity of each grid point; setting a disease diffusion rule, updating the state of each grid point according to the disease diffusion rule, and completing the prediction of anthracnose diffusion. According to the invention, by fusing multi-temporal unmanned aerial vehicle remote sensing monitoring data and continuous time sequence environment data, a space-time coupling model for describing a disease infection mechanism between adjacent grid points is constructed, and the spatial diffusion and evolution trend of diseases is dynamically simulated; the problems that a traditional disease prediction method is high in experience dependence, poor in spatial dynamic simulation capability, low in temporal-spatial resolution and the like are solved.

This invention discloses a method for predicting the spread of anthracnose in tea trees based on remote sensing imagery and grid analysis, comprising the following steps: acquiring multispectral images of tea plantations from unmanned aerial vehicles (UAVs), identifying soil background areas, healthy tea tree areas, and infected areas; dividing the tea plantation into a uniform grid space, defining the state of each grid point as either a healthy grid point or an infected grid point, setting a diffusion rate calculation method, and calculating the diffusion rate of each grid point; setting disease diffusion rules, updating the state of each grid point according to the disease diffusion rules, and completing the prediction of anthracnose spread. This invention, by fusing multi-temporal UAV remote sensing monitoring data and continuous time-series environmental data, constructs a spatiotemporal coupling model describing the disease infection mechanism between adjacent grid points, dynamically simulating the spatial spread and evolution trend of the disease, overcoming the problems of strong experience dependence, poor spatial dynamic simulation capability, and low spatiotemporal resolution of traditional disease prediction methods.

Disclosed are integrated DFOS systems and methods that advantageously integrates fiber sensing technology with advanced grid analysis to enhance the resilience of electrical distribution systems by providing accurate and efficient risk assessments. Utilizing real-time observations from Distributed Fiber Optic Sensing (DFOS), it accurately evaluates risks associated with probable events and calculates the risk of line failures. The method develops a modified risk-aware system that incorporates minimized system loss, voltage violations, power flow violations, the number of switching operations, and radiality constraints. By integrating DFOS data with existing grid data, the method enables rapid system adaptation and localized fault detection, advancing the state of the art in grid resilience and reliability.

The application discloses a kind of based on GIS's site vertical design earthwork quantity calculation method and system, it is related to earthwork quantity calculation technical field, including: according to topographic map, construct site present situation DEM;According to vertical design drawing, construct site design DEM;Site design DEM and present situation DEM are carried out grid analysis, obtain surface fill and dig earthwork grid data;Combined with control regulation data, calculate the surface fill and dig earthwork quantity of each plot of planning land;Calculate the underground engineering excavation quantity of each plot of planning land, building underground space, subway, underground comprehensive pipe gallery;The total amount of fill and dig of each plot of planning land is calculated, and fill and dig special map is generated.The system includes present situation DEM construction, design DEM construction, surface dig and fill quantity calculation, underground engineering excavation quantity calculation, total amount of dig and fill calculation 5 modules.The method is simple, efficient and accurate, low cost, wide application range, considering the underground engineering earth volume, the result is more reasonable and accurate, provide more reliable basis for site vertical design and construction.