436 results about "Boundary data" patented technology

The invention relates to a multi-model space-time combination flood peak prediction method fusing physical constraints, which comprises the following steps of: acquiring static space data, dynamic time sequence data and boundary data of a research drainage basin, converting the static space data of the digital elevation model into a grid matrix, and calculating the dynamic time sequence data of the research drainage basin according to the grid matrix and the dynamic time sequence data of the research drainage basin; representing an elevation value of each geographic position, extracting gradient features by using gradient calculation according to the elevation values, and performing normalization processing on the gradient features to obtain normalized gradient features; a CNN-Bi-LSTM-Transform prediction model is constructed, the prediction model comprises a spatio-temporal data alignment module, a CNN convolutional network, a spatio-temporal feature splicing module, a bidirectional long and short term memory network and a Transform module, the prediction model is trained, and a loss function during training adopts a physical constraint loss function composed of mean square error loss and water conservation constraint terms and is used for flood peak prediction.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

The invention relates to the technical field of chip testing, in particular to a testing method and system for a chip simulation model. The method comprises the following steps: collecting a target chip simulation model and performing time sequence compensation to generate jitter compensation data, then performing eye pattern reconstruction to obtain waveform eye pattern data, performing amplitude normalization and time sequence phase correction on the waveform data to obtain purified alignment data, and performing phase correction on the purified alignment data. The method comprises the following steps: generating main modal data through orthogonal mapping processing and modal projection analysis, constructing nuclear space data, carrying out manifold cognitive aggregation to generate test coverage features, carrying out boundary analysis and density distribution analysis on the features to obtain clustering boundary data, finally adjusting and optimizing boundary precision, executing an adaptive test sequence, and generating a test case result. According to the invention, a more efficient chip simulation model test method is realized.

The invention discloses a three-dimensional geologic model modeling method and device. The three-dimensional geological model modeling method comprises the following steps: acquiring to-be-established geological region data and actual geological boundary data; generating an initial stratum interface grid according to the to-be-established geological region data; correcting the initial stratigraphic interface grid to obtain a corrected stratigraphic interface grid; carrying out geological rule constraint on the corrected stratigraphic interface grid so as to form simple three-dimensional stratigraphic interface model data; correcting the simple three-dimensional stratigraphic interface model data according to the seismic profile information so as to obtain a composite geologic structure model; generating a three-dimensional fault plane model according to the composite geologic structure model; and generating a composite geological structure model including a fault network according to the three-dimensional fault plane model and the composite geological structure model. Through the three-dimensional geological model modeling method, the deviation between the model and an actual geological boundary can be remarkably reduced.

The invention relates to the technical field of safety management, in particular to an intelligent safety management system based on municipal engineering, which comprises a construction area risk dynamic assessment module, a hierarchical dynamic inspection frequency optimization module, an inspection path optimization module, an abnormal behavior monitoring module and a high-risk area early warning module. According to the method, quantitative safety assessment basis is provided for different types of operation by utilizing the environmental data of the construction area, combining with historical accident records and calculating the construction risk level, the inspection frequency is dynamically adjusted according to the risk level of the construction area, the self construction progress and abnormal records, so that the inspection resources are inclined to a high-risk area, and the inspection efficiency is improved. The polling efficiency is improved, meanwhile, the pertinence of safety management is enhanced, in combination with positioning information of constructors and boundary data of a high-risk area, the moving track of the constructors is analyzed, early warning is triggered when the constructors enter the high-risk area, safety risks are intervened in time, the safety supervision capacity of municipal engineering construction is improved, and the probability of construction safety accidents is reduced.

The invention discloses a submarine pipeline scour-vibration response analysis method based on a cross-scale coupling framework. The method comprises the following steps: establishing a multi-physics field coupling model according to geographical and structural information of a pipeline layout area and initializing the multi-physics field coupling model; marine environment data are obtained through real-time monitoring, the model is imported for scouring dynamic deduction, and a scouring boundary data set is generated. And reconstructing a multi-physics field solving environment, and forming a real-time physics solving environment package. After the model is updated, pipeline vibration response is analyzed, and a vibration data set is obtained. And performing fatigue damage analysis, deducing a flow guide field disturbance coefficient, updating the model again, performing next time step coupling calculation, and finally obtaining a full-period coupling analysis data set. And evaluating the pipeline operation safety situation based on the data set, and generating and pushing a safety report. Various physical field effects on the submarine pipeline in the marine environment are comprehensively considered, the safety of the submarine pipeline is evaluated more comprehensively, and cross-scale coupling analysis between the scouring effect and the vibration effect is achieved.

The invention relates to a multi-physics field coupling simulation method and equipment for industrial digital twinning. The method comprises the following steps: automatically dividing a plurality of heterogeneous physical sub-domains based on actual physical field data of industrial equipment, and defining a solving region and a boundary condition for each sub-domain; and then, a variable-driven region division mechanism is utilized to construct a parallel computing map of multiple physical domains, and efficient synchronization of inter-region coupling state variables and real-time interaction of boundary data are realized. In the parallel simulation process, calculation tasks and resources are dynamically and optimally distributed through a self-adaptive scheduling algorithm, bottleneck areas are effectively recognized and eliminated, and the overall simulation efficiency and stability are improved. According to the invention, co-evolution of a plurality of physical processes such as structure, heat, flow, magnetism, electricity and the like can be realized, and unified, flexible and extensible simulation support is provided for digital twinning, state monitoring, performance prediction, control optimization and other applications of a complex industrial system.

The invention relates to the technical field of computer networks, in particular to a bamboo-based composite material environment monitoring unmanned aerial vehicle and a networking method thereof, and the method comprises the following steps: each unmanned aerial vehicle obtains a residual electric quantity value and a three-dimensional coordinate position, actively broadcasts an identity beacon to the surrounding, and simultaneously monitors beacon signals from other unmanned aerial vehicles; for each received beacon signal, a transmitting node identity is extracted and a received signal power value is recorded, and an average signal strength indication value is calculated by continuously receiving a plurality of signal samples. According to the invention, the unmanned aerial vehicle actively broadcasts identity beacons and monitors surrounding beacon signals, masters neighbor node positions, energy levels and communication quality, and further completes comprehensive evaluation of initial network topology and node states; based on intelligent analysis of environmental data, pollution diffusion boundary data with high information value are distinguished, important information is ensured to be transmitted preferentially, and energy consumption of nodes is effectively balanced by calculating the relationship between the information value and energy consumption.

The invention discloses a method for predicting the service life of a road surface based on road construction indentation distribution, and belongs to the technical field of road data processing, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of the road surface, separating an indentation region from the three-dimensional point cloud data, and generating a first data set; acquiring image data from the first data set, and generating a second feature set containing indentation boundary data; calculating a point cloud height difference according to the height data of the first data set to obtain a third feature set containing depth feature data; inputting a current second feature set containing indentation boundary information and a third feature set containing depth features into a fourth prediction model, and outputting a material hardness prediction value; and calculating a pavement life prediction value according to the material hardness prediction value and the road load data. The method for predicting the road surface service life based on the road construction indentation distribution solves the problem that a traditional method is difficult to accurately evaluate the road surface indentation condition and predict the service life.

The invention discloses an elbow connector forward and reverse intelligent deviation rectification virtual assembly blanking method, which comprises the following steps: carrying out 3d scanning on a new elbow and two old pipes, carrying out space matching on two axes of the new elbow and the axes of the two old pipes, searching an optimal assembly position, calculating boundary data of an annular track and extracting track deviation rectification characteristics; the track deviation correction characteristics are added into the preliminary cutting control data so as to compensate the track installation deviation when the cutting robot cuts; and generating final cutting control data. The device has the beneficial effects that a new bent pipe and an old pipe are scanned in a handheld 3d mode, point cloud data are recognized, a cutting route is simulated, final cutting control data, the walking route for controlling the cutting robot and control data for controlling the cutting torch to move on the sliding rail are obtained, automatic cutting is achieved in cooperation with the cutting robot, and the cutting efficiency is improved. Therefore, data support is provided for efficiently completing pipeline first-aid repair.

The invention relates to the technical field of computer graphic processing, in particular to an image generation method and system, which refines the boundary recovery and structure fusion of an image through a multi-stage processing path, improves the quality and detail presentation of the image, and identifies the image by identifying the change in a boundary section by means of the gray gradient information of boundary pixels. A visual path in a shielding area is accurately extracted, calculation of the visual path, a gray value and a direction vector and screening of jump amplitude help to determine boundary recovery nodes in an image, visual performance of an image shielding part is effectively enhanced, an interruption position in the image is accurately calibrated in the process, and the image shielding efficiency is improved. The method lays a foundation for subsequent texture splitting and structure fusion, successfully divides the texture region in the image through texture direction and phase change analysis, accurately tracks the boundary trajectory of texture splitting, can optimize the texture structure in the image through splitting boundary data, and further improves the overall quality of the image through a variation region distribution diagram. The method realizes efficient reconstruction and fusion of image details, not only improves the recovery quality of the image, but also enhances the detail perception ability of the image, and especially shows great advantages in shielding and complex image processing scenes.

The invention provides an efficient transfer robot scheduling system for components in intelligent warehousing, and relates to the technical field of intelligent warehousing logistics, and the method comprises the steps: analyzing a transfer task instruction, extracting a task starting point coordinate, an end point coordinate and a path constraint condition, and collecting warehousing environment data in real time; the storage environment data comprises position offset state data, track boundary data and dynamic obstacle distance data; constructing a grid map model based on the position offset state data, the track boundary data and the dynamic obstacle distance data; and iteratively calculating a global final path node sequence on a magnetic stripe track by adopting a colistification algorithm fusing an oscillation factor disturbance mechanism and a self-adaptive weight strategy based on the grid map model. According to the invention, the problems of poor path flexibility and insufficient control precision in an intelligent warehousing component carrying system are solved.

The invention provides a building interior design layout optimization method, and relates to the technical field of indoor modeling and layout optimization, and the method comprises the steps: obtaining building structure boundary data and current situation layout information, constructing an indoor three-dimensional space model, and completing the marking of node passing attributes and a spatial topological structure. On the basis, crowd behavior data of a daily scene and an emergency scene are collected, behavior characteristics such as walking speed, obstacle avoidance response and congestion sensitivity are extracted, and a walking characteristic model used for simulating the individual motion trend is established. And performing multi-scene simulation under a daily event and an emergency evacuation event by using the walking feature model, calculating performance indexes such as passing time, local congestion degree, key node congestion probability and total evacuation time, and identifying a congestion sensitive area. And further constructing a multi-target evaluation model including traffic efficiency, congestion degree and evacuation performance, and performing global optimization on layout adjustable parameters by adopting an algorithm to obtain a candidate layout scheme set.

The invention discloses a regional carbon emission prediction method and system based on deep learning, and belongs to carbon emission prediction. The method comprises the following steps: acquiring historical carbon emission time sequence data and multi-dimensional constraint boundary data of a target area, and mapping the historical carbon emission time sequence data into an input feature vector; outputting an initial future carbon emission evolution trajectory vector; identifying and extracting a constraint violation dimension; constructing an input feature compensation tensor; generating an updated input feature vector; inputting the updated input feature vector into the depth space-time prediction model again for recursive prediction; and outputting a target carbon emission trajectory vector meeting the multi-dimensional constraint boundary data and a corresponding feature adjustment parameter set. According to the method, by automatically analyzing the multi-dimensional management constraint and fusing the multi-dimensional management constraint into the iterative optimization closed loop of the prediction model, the problem that the prediction model in the prior art only optimizes mathematical statistical errors and is seriously disjointed with multi-dimensional targets such as cost control and path feasibility in real management is solved.

The invention provides a method for simulating runoff in a cold and cold mountainous area by combining LSTM (Long Short Term Memory) to improve a VI-glacier model. The method is used for solving the technical problems that runoff components in the cold and cold mountainous area are diverse and each runoff source cannot be directly distinguished. The method comprises the following steps: firstly, determining model input data including DEM data, vegetation data, soil data, meteorological data, hydrological data and glacier boundary data; a GPM rainfall product is adopted to drive meteorological data; secondly, introducing a degree-day factor model to quantify the glacier ablation process, coupling the degree-day factor model with the VIC model, and constructing a VI-glacier model; then, an SCE-UA automatic optimization algorithm and an artificial trial and error method are combined to calibrate the VICs-glacier model, and an optimal parameter value is obtained; and meanwhile, error correction is carried out on a simulation result of the VI-glacier model through the LSTM model, so that runoff correction is realized. The model provided by the invention can relieve the uncertainty of water resources caused by glacier changes, and the future runoff can be predicted more accurately.

The invention relates to the technical field of city cadastral data analysis, and discloses a city update district boundary division method and system based on cadastral data, and the method comprises the steps: collecting the division associated cadastral data of a target city update district, and employing an improved DTW algorithm to obtain a city update district boundary; the method comprises the following steps: screening current situation boundary effective points and newly-added candidate points with history continuity from current situation measured data, extracting spatial features and ownership features for fusion, constructing a feature matrix by using a fusion feature vector, constructing a spatial ownership double-constraint graph neural network, predicting a final boundary point set of a target city update area, and obtaining a target city update area. And generating a final land parcel boundary. Therefore, by introducing the improved DTW algorithm and the spatial ownership double-constraint graph neural network, while accurate association of historical and current boundary data is established, joint optimization of spatial rationality and ownership rationality is realized by utilizing double-constraint feature fusion, and the division precision and division efficiency of the urban update district boundary boundary are improved.

The invention relates to a crop plot identification method and system based on time sequence vegetation characteristics, and the method comprises the steps: constructing a normalized difference vegetation index time sequence according to a multi-temporal satellite remote sensing image, and generating a crop probability distribution diagram through a crop classification model; performing connected domain analysis on the crop probability distribution map, extracting the center of gravity of an effective connected domain as a forward attention point, scanning the crop probability distribution map by using a sliding window, and generating a reverse attention point; based on the forward attention point and the reverse attention point, segmenting the high-resolution remote sensing base map through a visual segmentation model, generating a candidate mask set, and screening to reserve a mask with the maximum area as a land parcel segmentation mask of the forward attention point; and combining all the plot segmentation masks to generate a crop plot identification graph, and converting the boundary of each plot segmentation mask into a geodetic coordinate sequence to obtain plot vector boundary data. The method improves the recognition accuracy, and achieves the automatic and precise segmentation of the boundary of the land parcel.

The invention relates to the technical field of electric power simulation, and discloses an electric power system data parallel simulation processing method based on distributed modeling. The method comprises the following steps: acquiring voltage, frequency and power parameters of a new energy inverter and an energy storage converter, and generating a state switching feature vector through a fuzzy C-means algorithm; determining and calculating a load abrupt change subsystem according to the load index overrun, and calculating the electrical coupling strength with an adjacent subsystem; allocating reserved computing resources for the mutation subsystem through a particle swarm optimization algorithm to form a dynamic resource allocation table; and adjusting the data synchronization period of the mutation subsystem to an extended time window, and continuing parallel simulation after the adjacent subsystems wait for boundary data. According to the method, the problems that in an existing power system data parallel simulation processing method based on distributed modeling, the calculation load abrupt change and the data synchronization failure caused by strong nonlinear component state switching cannot be processed are solved.

The invention provides a high-cold and high-altitude environment-oriented energy storage battery state monitoring method and system, and the method comprises the steps: innovatively constructing a multi-scale physical constraint framework: extracting multi-dimensional features through a cavity convolution network with a time sequence perception capability, and fusing the initial state physical information of a battery; embedding inherent physical law constraints of the battery into the characteristic space, wherein the inherent physical law constraints comprise forced charge conservation, health state one-way attenuation and temperature and pressure self-adaptive stability control; a special compensation mechanism is designed for an extreme environment, accurate state estimation is realized by dynamically correcting a low-temperature diffusion behavior, and lithium precipitation safety early warning triggered by multi-condition collaboration is established. A calibration-free training strategy is adopted, model optimization is completed only through a small amount of boundary data, and adaptability is enhanced and improved in combination with plateau environment data. And the method can be operated in a resource-limited embedded platform through lightweight processing. And under the working conditions of high and cold and low air pressure, the core industry problems of insufficient adaptability to extreme environments, misalignment of physical rules, limited hardware deployment and the like are effectively solved.

The invention relates to the technical field of intelligent water conservancy and automatic control, in particular to a polder area multi-station water conservancy data real-time collection and intelligent fusion processing method and system, and the method comprises the steps: a multi-dimensional collection system construction step: collecting time sequence state, fluid field and environment boundary data, and executing time base unified cleaning; an ideal model construction step: constructing an ideal water tank model based on the law of conservation of mass and generating an ideal water level change curve; a parameter injection simulation step: generating a simulation fault water level curve according to injection efficiency attenuation and medium leakage parameters; a double-track difference calculation step: respectively carrying out difference on actual measurement and simulation data and an ideal curve to generate real and theoretical residual vectors; a similarity judgment and control step: calculating sequence similarity to judge a real physical fault or sensor data noise, and generating a fusion control instruction; according to the invention, the technical problem of distinguishing the noise of the sensor from the real physical fault is effectively solved, and false alarm or missing alarm is avoided.

The invention discloses a defect detection method and device based on a dynamic dual-threshold template, and a storage medium, and aims to improve the defect detection precision of an image shot by a display screen. Collecting a plurality of batches of defect-free sample images of the target model display screen; counting gray extreme value boundary data of each pixel point on the plurality of batches of defect-free sample images; and generating a lower limit standard template and an upper limit standard template according to the gray extreme value boundary data. And obtaining a regional difference amplification coefficient and a compensation offset. And generating a dynamic detection threshold template for the lower limit standard template and the upper limit standard template through the regional difference amplification coefficient and the compensation offset. And obtaining a to-be-tested screen body image of the target model display screen. And carrying out pixel-level difference on the to-be-detected screen body image and the dynamic detection threshold template to generate a defect mask image. And performing defect detection on the display screen through the defect mask image to generate a first defect detection result.

The invention discloses a flexible climbing joint clearing method and system based on stochastic optimization, and a related device, and belongs to the technical field of power system optimization scheduling, and the method comprises the steps: obtaining clearing boundary data, and completing verification; based on the historical data of the new energy predicted power and the load power predicted value, generating a random scene considering the wind and light uncertainty, and performing scene reduction; and inputting the verified clearing boundary data and the reduced random scene considering the wind and light uncertainty into a pre-constructed flexible climbing and electric energy, standby and frequency modulation combined clearing optimization model, and solving to obtain a clearing result of flexible climbing, electric energy, standby and frequency modulation. According to the method, coupling mutual exclusion relation characteristics of electric energy, flexible climbing, standby and frequency modulation in the aspects of unit capacity, unit climbing capacity and the like are combined, optimal configuration of resources is achieved, uncertain factors of a complete link of power production scheduling are represented in a refined mode, and robustness of clearing results of electric energy, flexible climbing, standby and frequency modulation can be improved to a great extent.

The invention belongs to the technical field of mine safety mixed reality interactive training, and particularly provides a mine safety training detection method and system based on mixed reality, and the method mainly comprises the steps: obtaining a mine basic data set, and generating enhanced safety constraint boundary data according to the mine basic data set; generating environmental risk features according to the enhanced security constraint boundary data; generating security behavior features according to the enhanced security constraint boundary data and the environmental risk features; generating risk evolution characteristics according to the environmental risk characteristics and the safety behavior characteristics; generating emergency response characteristics according to the safety behavior characteristics and the risk evolution characteristics; and generating a dynamic risk visualization picture according to the risk evolution characteristics and the emergency response characteristics. According to the method, training scene dynamic, operation behavior compliance detection automation and risk evolution process visualization are realized, and safety training effectiveness and emergency processing capability are improved.

The invention relates to the technical field of state detection and equipment fault early warning based on artificial intelligence, and discloses a state detection and equipment fault early warning method based on artificial intelligence. Three independent sensors are installed at key parts of the equipment to collect temperature, vibration and pressure data respectively, and data comprehensiveness and numerical value consistency are ensured. And a sampling time discrete sequence number and an original data function are set, so that data acquisition time sequences are consistent, and data integrity in each update period is ensured. And calculating a local adjacent point mean value to reduce instantaneous noise. And by calculating a deviation amount, quantifying local fluctuation of the data, and identifying an abnormal symptom. And constructing a smooth function, carrying out nonlinear compression on deviation, and reducing the influence of extreme values. Boundary data are processed through symmetric continuation, and smooth calculation is prevented from being affected by data missing when a sampling period starts and ends. The data are normalized after the minimum value and the maximum value are calculated, the data scale is unified, and the accuracy of multi-sensor data fusion and comparison is improved.

To provide a three-dimensional geological model correction device, method, and program that can quickly and effectively correct a three-dimensional geological model using strata information obtained from face observation.SOLUTION: A three-dimensional geological model correction device comprises: three-dimensional geological model generation means 30 for generating a three-dimensional geological model of a construction target including three-dimensional coordinates and three-dimensional stratum boundary data based on previously acquired stratum information of the construction target; face image acquisition means 21 for acquiring a face image of an already excavated area; face coordinate acquisition means 22 for acquiring three-dimensional coordinates of the face image; two-dimensional stratum boundary data extraction means 23 for extracting two-dimensional stratum boundary data from the face image; identical boundary extraction means 40 for extracting identical boundaries based on proximity of three-dimensional coordinates of boundary lines constituting the three-dimensional stratum boundary data and the boundary lines constituting the two-dimensional stratum boundary data; and three-dimensional stratum boundary data correction means 50 for correcting the three-dimensional stratum boundary data based on the two-dimensional stratum boundary data related to the extracted identical boundaries.SELECTED DRAWING: Figure 1

Disclosed in the embodiments of the present disclosure are a vehicle control method and apparatus. The method relates to the fields of autonomous driving technology and autonomous vehicles, and comprises: in response to receiving first spatial information of a first workstation, acquiring first sensing information of a first vehicle, wherein the first spatial information is generated on the basis of initial map boundary data, and both the first sensing information and the initial map boundary data are used for determining the boundary of a working area; adjusting the first spatial information on the basis of the first sensing information, so as to obtain second spatial information of the first workstation, wherein the degree of matching between the second spatial information and the boundary of the working area is greater than the degree of matching between the first spatial information and the boundary of the working area; and generating a first control instruction on the basis of the second spatial information, wherein the first control instruction is used for controlling the first vehicle to travel to the first workstation. The present disclosure solves the technical problem in the prior art of the control precision of an autonomous mining truck being reduced and thus affecting the working efficiency due to map updates being affected by network conditions.

A method for recognizing a boundary object of a road includes sensing an environment around a vehicle by an environment perception sensor that includes a laser scanning-based three-dimensional recognition sensor and a sensor of a different type. The method also includes determining candidate detection data from abnormal detection data that is perceived to be abnormal by the three-dimensional recognition sensor and is unassociated with an internal static object of a road and an internal dynamic object of the road, by referring to a boundary region. The method also includes adopting estimated boundary data that is considered as a road boundary object in the candidate detection data, based on distribution information of normal boundary data that is normally perceived in relation to the road boundary object by the three-dimensional recognition sensor. The method also includes employing the normal boundary data and the estimated boundary data as boundary data.

The invention relates to a two-dimensional and three-dimensional boundary point association method based on improved boundary point coding and a medium, and the method comprises the steps: carrying out the preprocessing of original land parcel red line data, sequentially numbering land parcels, converting all the land parcels into plane boundary point elements, determining a first boundary point in each plane boundary point element, processing each land parcel into a clockwise land parcel, and carrying out the coding of the clockwise land parcels. Performing two-dimensional boundary point continuous numbering on all vertexes of the land parcel, taking the land parcel after encoding the two-dimensional boundary points as a two-dimensional surface element, and converting the land parcel into a land parcel three-dimensional stereoscopic model comprising three-dimensional boundary points, boundary lines and boundary surfaces; and continuously numbering all the three-dimensional boundary points in sequence by using a cross section analysis method to obtain a land block after coding the three-dimensional boundary points, and establishing an association relationship between the three-dimensional boundary points with successfully matched coordinates and the corresponding two-dimensional boundary points. Therefore, automatic coding of the three-dimensional boundary points of the land parcel red line data and automatic association between the two-dimensional boundary points and the three-dimensional boundary points are realized, and the three-dimensional boundary point management efficiency in land parcel management is improved.

The invention relates to the technical field of power grid data management, and provides a power grid boundary data processing method and device, a power grid boundary data processing system, a readable storage medium and a program product. The method comprises the following steps: mapping each piece of power grid boundary data into standard boundary data; determining a multi-dimensional identifier of each standard boundary data according to a regulation and control object and a scheduling version of the power grid boundary data to obtain compressed boundary data corresponding to the same multi-dimensional identifier; determining the compressed boundary data of the same scheduling version according to the multi-dimensional identifier of the compressed boundary data to obtain a boundary data snapshot of the corresponding scheduling version, and storing the boundary data snapshot in a snapshot database; when a query request is received, a first boundary data snapshot set is determined in the snapshot database, and then a second boundary data snapshot set is obtained to form a query result. By adopting the method, complex multi-condition screening and multi-granularity time window analysis are supported.