509 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 relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

The invention discloses an ophthalmologic operation real-time navigation system and method based on dynamic visual field tracking, and relates to the technical field of medical systems. Comprising the following steps: matching operation data closest to a current operation from a database, and generating standard operation navigation of the current operation; the real-time image of the operation process is compared with the image of the standard operation navigation in real time, the step of the current operation is determined, and meanwhile subsequent image navigation of the operation of the patient is displayed; generating a spatial mapping matrix between an eyeball movement track and a target region by combining target region boundary data of preoperative OCT image segmentation and an intra-operative navigation coordinate system; the virtual navigation path of the surgical instrument is dynamically corrected according to the real-time eyeball movement track, so that the surgical instrument is aligned with the target area, the spatial relation between the instrument and the target area can be visually mastered through the overlapping display of the three-dimensional virtual path and the real-time image, unconscious displacement or physiological tremor of a patient in an operation is actively adapted, and the operation accuracy is improved. And the continuous accuracy of the navigation path is ensured.

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 PINNs high Reynolds number flow field solving method based on regulatory factor optimization, and the scheme comprises the steps: firstly building a physical model, on one hand, building a geometric model according to the actual condition of a reactor core rod bundle channel, and on the other hand, building a fluid mechanics model containing continuity, momentum conservation and energy conservation equations; a data set is obtained, and experimental data is preprocessed and then integrated with simulated data through experimental measurement and numerical simulation; and then training a physical information neural network, designing a neural network model of specific input and output, introducing a regulatory factor to balance the difference between a physical equation and data, constructing a comprehensive loss function containing internal data points, boundary data points and equation loss, and training the network by using a training data set until convergence. And finally, solving a flow field by using the trained neural network, outputting predicted values of a velocity field, a pressure field and a temperature field, and solving the high Reynolds number flow field of the reactor core.

The invention relates to the technical field of image processing, in particular to an integrated image restoration method and system based on image mask modeling, and the method comprises the following steps: collecting the boundary data of the whole image, separating the boundary of a mask region from the boundary of a non-mask region, and analyzing the gradient change characteristics of the boundary of the mask region. And extracting local depth information of the mask region from the gradient change features, and constructing a mask region boundary residual distribution model. According to the method, the mask region and the non-mask region are separated, the boundary gradient features are extracted, the residual distribution model is established on the basis of gradient change, the subtle change of the boundary of the mask region is accurately captured, and the accuracy of the boundary features is improved. By constructing a depth information data set and combining local depth information with gradient features, adjustment of layered dynamic weight distribution of a mask area basic structure is realized, effective extension of area features to the inside is ensured, and the possibility of boundary information loss is reduced.

Embodiments of the invention disclose a process parameter data determination method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining preset parameter data of preset process parameters and parameter boundary data of to-be-predicted process parameters; performing initialization processing on the population of the genetic algorithm according to the preset parameter data and the parameter boundary data, each individual in the obtained initial population being used for indicating a group of process parameter data; inputting the process parameter data corresponding to the initial population into a pre-trained proxy model to obtain a profile tolerance deviation predicted value; calculating a fitness value according to the profile tolerance deviation predicted value; and performing optimization iteration on the initial population according to the fitness value, taking the maximization of the fitness value as an iterative optimization target until the number of iterations reaches a preset number of times, and outputting process parameter data of the target individual.

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 belongs to an aircraft wire harness design technology, and relates to a digital and automatic collaborative design simulation method for aircraft wire harnesses based on Capal and CATIA. According to the method, an existing boundary data model is utilized, electrical parts are installed on CATIA software, and wiring harness arrangement environment conditions are created; creating a three-dimensional wire harness model in the CATIA software; establishing a Capal graphic symbol library and a wire harness part library; creating an aircraft electrical wiring diagram on the Capital software by using the graphic symbol library and the wiring harness part library file; in combination with a CATIA three-dimensional wiring harness model, interactive design is carried out with flight Capital software, and an aircraft two-dimensional topological graph is formed; the sectional area of the wire harness is automatically calculated through CATIA software, and an aircraft simulation three-dimensional wire harness is formed; and combining the aircraft simulation three-dimensional wiring harness with the aircraft topological graph and the aircraft wiring harness two-dimensional graph design to form an aircraft two-dimensional wiring harness chart diagram. According to the invention, database management informatization, three-dimensional wiring harness design digitalization, two-dimensional electrical system principle design and three-dimensional electrical wiring harness model interaction design automation are realized.

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 relates to the technical field of medical image processing, in particular to a medical image diagnosis method and system based on a cloud platform, and the method comprises the following steps: based on an input medical image, analyzing the texture density of a plurality of positions in the image, calculating the gray gradient change direction and rate of pixel points, and extracting boundary points of a focus region in the image; segmenting the lesion image to obtain lesion boundary data; according to the method, the accurate segmentation of the lesion area is realized by calculating the texture density of a plurality of positions in the image, and the spatial distribution of the lesion is predicted by using the tissue density value of the lesion area and the density change rate between adjacent pixels, so that the dynamic change of the lesion area is quantified, and the dynamic image sequence is analyzed; the lesion activity level grading is realized, the lesion development trend is more intuitive, the resolution of the image loading area is dynamically adjusted in combination with real-time user operation and the network state, the remote image access experience is optimized, and the image calling is more efficient and smoother.

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 relates to the technical field of water quality monitoring and drainage basin water environment management, in particular to a drainage basin water quality prediction method and system based on a spatial flow river network topological structure, and the method comprises the steps: firstly, determining a monitoring region, obtaining drainage basin digital elevation model (DEM) data and drainage basin boundary data, and then obtaining water quality data in a drainage basin through setting a monitoring point; the method comprises the following steps: firstly, mainly monitoring an area with relatively large water quality change, then obtaining an environment variable in a drainage basin, analyzing and generating an accurate prediction river network and a prediction point in combination with a spatial flow river network topological structure, then predicting water quality parameters by constructing a spatial flow river network topological structure model and combining water quality data of a monitoring point and the environment variable, and finally, predicting the water quality parameters by constructing a spatial flow river network topological structure model. A prediction result is displayed through a geographic information system (GIS) visual tool, real-time monitoring and water quality early warning are facilitated, the technical scheme is suitable for water quality prediction of a large-scale drainage basin, the change of the water quality of the drainage basin can be monitored in real time, and a scientific basis is provided for drainage basin management and pollution prevention and control.

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 discloses a data management method and system for land conservation and intensive utilization, and relates to the technical field of geographic information data management application, and the method comprises the following steps: obtaining multi-source heterogeneous land supply data, and obtaining a territorial resource database; establishing a dynamic update database for unavailable and idle lands which are not treated completely; organizing and managing a land-related database and red line boundary data, and constructing a graph database; and combining the dynamic update database, the dynamic update database and the one-map database, and dynamically checking and disposing the batch, unsupplied and idle land. According to the method, through a spatialization means, space precision control is carried out in combination with the coordinates of the land supply boundary points, space conflict detection is carried out on the reported boundary points firstly, it is guaranteed to the greatest extent that no large conflict range exists between the uploaded land parcel range and other land parcels, and meanwhile, the problem of batch supply consistency is guaranteed, that is, the land supply range must be within the batch range.

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 discloses a Markov chain-based power dispatching boundary data behavior anomaly detection method and system. The method comprises the following steps: monitoring historical data of each information object of network boundary equipment of a power dispatching system in an IEC104 protocol, and extracting each dispatching service according to a time interval of a remote control instruction data packet of the information object; constructing a state transition matrix based on all historical remote control instructions for each information object based on a Markov chain algorithm, wherein the state transition matrix records all possible transitions among states and probability distribution thereof; when a new scheduling service is received, calculating the probability that the scheduling service belongs to a normal behavior by using the state transition matrix, and if the calculated probability is lower than a preset safety threshold, determining that the scheduling service has an abnormal behavior; and updating the corresponding state transition matrix according to the abnormal behavior detection result. According to the scheme, the anomaly detection capability of the power dispatching control system is remarkably improved.

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 discloses a full-coverage path planning method for a high-clearance sprayer under an irregular convex field block based on a particle swarm algorithm. The method is characterized by comprising the following steps: acquiring boundary data of a farmland area to obtain a boundary contour of an irregular convex field block; secondly, on the basis of a traditional U-shaped turning mode, an included angle between each operation row and the boundary of the field is introduced, and the connection path principle between the operation rows is elaborated in detail; the method comprises the following steps of: performing parallel line offset on a straight line passing through a central point of an irregular convex region, generating a full-coverage operation row with a random direction angle, giving different weights of three parameters, namely total length of an agricultural machine operation path, turning times and operation coverage rate, optimizing the direction angle of the operation row through an improved particle swarm optimization algorithm, and finally obtaining a full-coverage operation row with a random direction angle. According to the scheme, the energy consumption of the unmanned agricultural machine is effectively reduced, the working efficiency is improved, and the basic requirement of agricultural production for full-coverage path planning can be met.

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 the technical field of cost management, in particular to an enterprise cost control method and system based on big data, and the method comprises the following steps: detecting the calling record and use frequency of enterprise business resources, and recognizing key fund inflow and outflow nodes according to the flow direction frequency of a fund flow path to obtain cost classification boundary data. According to the invention, dynamic management and optimization of real-time cost data are realized through comprehensive analysis of enterprise resource calling data and fund flow direction, and tracking accuracy of resource utilization is improved through real-time monitoring of calling frequency and fund flow direction of service nodes, so that a cost control process is more transparent and predictable. According to the method, the cost items can be dynamically matched and optimized by using the time stamp and the related data of the fund flow, the accurate adjustment of the cost control is realized, in addition, the introduction of the dynamic cost distribution index allows the enterprise to quickly respond in the changing market environment, and the cost management error caused by the market fluctuation is effectively reduced.

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.