253 results about "Boundary constraints" patented technology

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

The invention relates to the technical field of building design and digitization, and discloses an automatic design method for a special-shaped facing structure based on parametric modeling. The method comprises the following steps: firstly, acquiring a geometrical characteristic parameter set of the special-shaped facing structure, wherein the geometrical characteristic parameter set comprises curved surface curvature distribution, boundary constraint conditions and a material attribute threshold value; thirdly, according to the curved surface curvature distribution, generating an initial geometric topological grid by using a dynamic subdivision algorithm, adjusting node positions according to boundary constraint conditions to obtain an optimized structural framework, and performing layered mapping on material attribute thresholds to generate a material distribution map; then, the input parametric modeling engine generates a three-dimensional parametric model, finally, a self-adaptive adjustment strategy graph is constructed through a multi-objective optimization algorithm, and a final design scheme is output. By means of the method, automation of design of the special-shaped facing structure is achieved, design efficiency and accuracy are improved, material utilization is optimized, scheme adaptability is enhanced, and design process collaboration is improved.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses a dynamic DEM spatial interpolation method which comprises the following steps: performing depression filling, flow direction analysis and confluence cumulant calculation on DEM data, and extracting a natural sub-basin unit by adopting a minimum catchment area threshold method; constructing a topographic feature matrix, performing refined second-level classification on the first-level drainage basin through an improved self-organizing mapping network, generating a hydrological response unit through boundary processing, and establishing a hydrological attribute library; fusing multi-source data, supplementing attribute interpolation such as underlying surface and rainfall, and constructing an interpolation auxiliary parameter system; a drainage basin is divided into regular grids as neurons, a dynamic neural network containing dynamic states and static attributes is constructed, and nonlinear mapping of DEM correction parameters is achieved through optimization of a dynamic activation function and a loss function. The method overcomes the defects that a traditional interpolation algorithm does not consider hydrological boundary constraints, a neural network model topological structure is fixed and the like, and the DEM interpolation precision and the hydrological simulation effect of the complex terrain area are improved.

The invention relates to the technical field of artificial intelligence, in particular to a sheet part performance rapid prediction method based on deep learning, which comprises the following steps: collecting multi-working condition simulation data to generate a training sample, constructing and coding a grid topological structure to extract multi-dimensional features, and inputting a perceptron to predict stress and evaluate errors after feature fusion and self-attention mechanism processing. According to the method, a structured training sample set is constructed by introducing simulation information, a geometric structure feature set is formed by combining node space coordinates, boundary constraints and a connection relation, so that mutual positions and constraint conditions among nodes are completely expressed in a graph structure, and through node-level feature extraction and feature fusion processing, a graph structure is obtained. According to the method, deep embedding of node geometric layout and boundary interrelation is realized, learnable expression of a stress evolution path in a space structure is established through local subgraph and context analysis, a multi-layer feature aggregation and attention mechanism is introduced in a node graph embedding process, and feature response expression of a key area is enhanced.

The invention relates to the technical field of tunnel engineering safety, in particular to a tunnel fire multi-stage damage collaborative prediction method and system, and the method comprises the steps: constructing a data simulation fusion model, and carrying out the processing of fire and tunnel structure parameter data, and obtaining a multi-working-condition parameter database; constructing a heat conduction equation, a fire plume and wall surface heat exchange relational expression, a boundary layer correction model and a comprehensive damage evaluation model, and analyzing the equation, the relational expression and the model by utilizing a multi-physical field coupling unified boundary condition to obtain a multi-physical boundary constraint condition; obtaining a multi-physical constraint loss function based on a network architecture and a multi-physical boundary constraint condition, and further obtaining a comprehensive constraint loss function of a temperature field-stress field-damage index; constructing a PINN-LSTM network architecture and a damage evolution analysis model based on the fire ending initial state coding vector, the fire course feature vector and the cooling process feature vector; and performing linkage analysis on the function, the frame and the model to predict and monitor the transient damage and the long-term creep damage of the tunnel.

The invention discloses a global reference line generation method for an automatic driving vehicle and a related device, and relates to the technical field of automatic driving navigation, and the method comprises the steps: generating a collision-free original path of the vehicle based on an improved A * algorithm according to a starting point position and a terminal point position of the vehicle in a grid map; removing redundant points in the collision-free original path of the vehicle to obtain an original reference path; numerical smooth optimization is carried out on discrete points of the original reference path, and a cubic spline interpolation method is adopted to generate a basic reference line meeting the geometric continuity of the vehicle; establishing a Frenet coordinate system based on the basic reference line, and carrying out transverse and longitudinal motion primitive sampling to obtain a vehicle kinematics constraint and a boundary constraint; according to the constraint, establishing a quadratic programming multi-objective optimization function; and solving the quadratic programming multi-objective optimization function to obtain a global reference line. According to the invention, the robustness and security of reference line generation in a complex scene can be improved.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention discloses a track optimization control method and system for a high-speed linear seven-axis manipulator, and relates to the technical field of data processing. The method comprises the following steps: acquiring a multi-manipulator grabbing task, dividing the task into single-manipulator grabbing sub-tasks, defining a starting point and an ending point for each sub-task, and generating a track boundary constraint set through a constraint hierarchical model; taking the termination point as a root node, generating a reverse extension tree in a feasible region range of the trajectory boundary constraint set through a potential field steering strategy, and outputting a multi-dimensional trajectory primitive; the multi-dimensional trajectory primitives are preprocessed, and a candidate trajectory set is screened out through a multi-objective optimization strategy; and integrating the optimized trajectories of the manipulators through a global trajectory conflict coordination mechanism, and detecting trajectory conflicts by adopting space-time alignment to generate a global optimal trajectory.

The invention combines the advantages of priori knowledge fusion, provides an accidental explosion concrete penetration depth prediction method fused with experience algorithm knowledge, and aims to improve the prediction precision and physical rationality in a small sample scene. The method comprises the following steps: firstly, constructing a combined parameter Z through a physical parameter, a target attribute and a Forrest formula, and taking the combined parameter Z as an input feature of a model to represent a physical rule; then, a multi-objective loss function is designed, the multi-objective loss function comprises a data fitting item, a physical constraint item and a boundary constraint item, the physical constraint item verifies the physical consistency of predicted values through trace disturbance input parameters, and the boundary constraint item ensures that the penetration depth is non-negative. And further constructing a dynamic weight adjustment mechanism, and automatically balancing the optimal weight of the physical law and data fitting according to the training process. And based on a back propagation neural network framework, performing model training in combination with the constraints. According to the method, the mean square error is remarkably reduced in a concrete penetration task, and meanwhile, the physical interpretability of a data-driven model is expanded.

The invention discloses a high-formwork optimization design method and system based on BIM and finite element analysis, and the method comprises the steps: S100, building a three-dimensional BIM model of a main body structure, and creating an intelligent parameterization frame body family which is mapped with a main body structure family and has a self-verification function; s200, importing a parameterized family and linking the main body model, generating a plurality of groups of frame body arrangement schemes through spatial topology analysis, and performing a three-dimensional collision test after optimization; s300, setting dynamic boundary constraints and construction loads in stages, and generating a geometric and mechanical integrated data packet; s400, data packet parameters are automatically mapped to finite element software through a plug-in, models are built in stages, multi-working-condition analysis is carried out, if the parameters exceed the limit, frame body parameters are modified, iterative optimization is carried out until the parameters meet the standard, and a result is synchronized to a three-dimensional BIM model; and S500, a construction drawing is automatically generated, and visual disclosure and high formwork monitoring point selection are carried out. According to the method, design, computational analysis and construction application data linkage and closed-loop management from design to construction to monitoring are achieved, and the high formwork design efficiency and safety are improved.

The invention relates to an active power regulation and control method and system based on boundary constraint, and the method comprises the following steps: constructing a boundary constraint condition model, and achieving the dynamic updating of the boundary constraint condition model through a visual maintenance tool; the load rate of the power grid equipment is monitored in real time, whether the power grid equipment is out of limit is judged according to the scene definition table, working condition classification is performed, and generation of a control strategy is triggered; a control strategy is generated based on sensitivity analysis and resource priority, and a comprehensive index calculation model is adopted to screen and adjust resources according to a strategy constraint definition table; sensitivity analysis obtains a sensitivity coefficient based on real-time topology of power grid equipment in combination with load flow calculation, and the resource priority is determined according to a resource rule definition table; and when a single type of resources cannot meet the regulation and control requirements, generating a comprehensive control strategy containing multi-type resource regulation variables, and executing closed-loop effect verification and strategy iteration through a strategy index definition table.

The invention provides a coal ash composite material goaf closed filling parameter intelligent matching method, and belongs to the technical field of deep learning and mining engineering crossing. According to the method, dynamic optimization and accurate matching of the filling parameters are realized through combination of data driving and an intelligent algorithm. The method comprises four core links: multi-source data perception and fusion, a material performance prediction model, key parameter identification and boundary constraint, and intelligent matching and optimization decision. According to the method, the modeling capability of the model for the complex coupling relationship is improved through the attention mechanism and the feature cross network; the performance evolution trend of the material under different proportions and process conditions is accurately predicted, key regulation and control parameters are automatically identified, and efficient search and optimal matching of a parameter space are achieved through a strategy gradient method. According to the method, multi-source data can be fused, key parameters can be dynamically identified, and the intelligent matching and optimizing capability is achieved.

The invention discloses a method for generating a reference line in vehicle driving path planning and electronic equipment, and the method comprises the steps: obtaining initial data corresponding to an automatic driving vehicle, the initial data comprising driving map information, historical driving track information and vehicle parameter information; preprocessing the initial data to obtain target data; according to the target data, constructing boundary constraint information required by optimization based on the environment information; according to the initial reference line and the target boundary constraint information, generating target input information corresponding to a target reference line optimizer, and inputting the target input information to the target reference line optimizer, so that the target reference line optimizer performs reference line optimization processing according to the target input information to generate corresponding target output information, the target output information comprises a target reference line of the autonomous vehicle corresponding to a target driving road in the driving environment. Therefore, the reasonability and accuracy of the reference line can be improved.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a power grid demand side resource adjustable power domain aggregation method and device. The method comprises the following steps: S1, constructing adjustable power domains of various demand side resources containing time sequence coupling characteristic constraints, and representing the adjustable power domains of the demand side resources in adjacent time periods by using time-segmented linear boundary constraints; s2, establishing a demand side resource comprehensive probability model containing various correlations according to historical data of various demand side resources; and S3, constructing an adjustable power domain aggregation model of the demand side resources in combination with the linear boundary constraint and the comprehensive probability model, and obtaining a flexibility decision space of the demand side resources through model optimization. According to the scheme, the linear constraint of each demand side resource and the correlation constraint between the demand side resources are considered at the same time, the obtained feasible region is finer, the supply and demand regulation and control effect of the new energy transformer area is improved, and the stability and safety of a power grid are improved.

The invention provides a CAD (Computer Aided Design) drawing error recognition method utilizing deep learning, which relates to the technical field of image data processing and comprises the following steps of: constructing a primitive structure by reading primitive information of a CAD drawing; extracting image features of the drawing based on the feature extraction network to perform semantic modeling on the primitive structure to obtain a primitive structure semantic model; identifying a nested primitive pair; performing edge detection and contour fitting on the nested primitives, and extracting nested edge coordinate points; and obtaining nested edge coordinate points of the nested primitives, generating a boundary constraint range by using the nested edge coordinate points to carry out error recognition on the nested edge coordinate points, and carrying out interactive feedback correction according to an output error recognition result. According to the method and the device, the technical problem that errors in a nested structure are difficult to find timely and accurately due to the lack of intelligent recognition capability for the primitive nesting relationship in the prior art is solved, and the technical effect of improving the CAD drawing error recognition accuracy is achieved.

The invention discloses a SiC MOSFET service life prediction method and device, equipment and a medium, and relates to the technical field of health detection of power electronic devices. The method comprises the following steps: acquiring an acquisition signal set acquired by a multi-channel sensor; the collected signals are preprocessed, smoothing and fusion estimation are carried out by adopting an extended Kalman filtering algorithm, and multi-dimensional degradation features including drain-source conduction voltage drift rate and thermal resistance change rate are extracted through feature engineering. And carrying out global optimization on the key hyper-parameters and the initial weight of the LSTM network by adopting an improved artificial fish swarm algorithm. The algorithm comprises an adaptive optimization strategy, a mixed local search operator and a boundary constraint processing mechanism. And training the optimized LSTM network by using the multi-dimensional degradation characteristics, establishing a mapping relationship between the multi-dimensional degradation characteristics and the device life, and obtaining a trained prediction model. And acquiring a to-be-predicted acquisition signal set to extract multi-dimensional degradation features and input the multi-dimensional degradation features into the prediction model to obtain a residual life prediction result.

The invention belongs to the technical field of power system dispatching and control, and particularly discloses a scenarized sample editing method and a related device. The scenarized sample editing method comprises the following steps: determining a boundary constraint condition, and carrying out optimization adjustment on the determined boundary constraint condition by utilizing an optimization algorithm to obtain an optimized constraint condition; modifying and adjusting a pre-obtained original historical data sample to obtain an edited sample; and carrying out calculation arrangement and simulation operation on the obtained edited sample, carrying out comparison verification on a result and boundary constraint conditions, if all the boundary constraint conditions are met, outputting the edited sample as a scenarized sample of a selected scene, and if the boundary constraint conditions are not met, outputting the edited sample as a scenarized sample of the selected scene. Otherwise, determining the boundary constraint condition again according to the actual operation condition of the power grid and the selected scene demand. According to the method, effective construction and editing of the scenarized sample are realized, and the efficiency and the accuracy are relatively high.

The invention provides a trajectory planning method and device based on a space-time corridor and an automatic driving vehicle, relates to the technical field of computers, in particular to the fields of automatic driving, path and trajectory planning and the like, and can be used for trajectory planning, obstacle modeling and other application scenes. According to the specific implementation scheme, a space-time semantic corridor is constructed in a three-dimensional space-time coordinate system according to semantic information of a road reference line and a driving environment, and a motion track to be optimized is generated; matching a space-time corridor unit corresponding to the coordinates of the current track point from the space-time semantic corridor according to the to-be-optimized motion track, and constructing corridor boundary constraints based on the space-time corridor unit and the punishment cost; according to the road reference line and the motion trail to be optimized, constructing a trail following cost; constructing a trajectory optimization model according to the corridor boundary constraint, the trajectory following cost and a preset vehicle kinematics constraint; and solving the trajectory optimization model by using a hierarchical optimization algorithm to obtain a planned trajectory. According to the scheme, the trajectory planning speed and quality can be improved.

The invention relates to the technical field of underwater topographic surveying and mapping, in particular to a sea area water taking channel scanning survey method and system based on multi-beam measurement, and the method comprises the following steps: constructing a rigid support, fixing a depth finder through a three-way cable fastening device, and building a Boolean parameter model; sound velocity, tide level and original echo data are collected; performing space-time registration, coordinate conversion and filtering denoising on the data to generate corrected water depth; and constructing an irregular triangulation network by using hard boundary constraint, generating a digital elevation model, and extracting a siltation area. According to the invention, multi-beam wide sector scanning and multi-sensor attitude correction technologies are fused, a limited water area full-coverage measurement system is constructed, and the problems of terrain blind areas and detail loss are solved. A boundary constraint reconstruction algorithm is utilized to realize closed-loop processing from coordinate reference unification to micro-landform restoration, the accuracy of channel bottom pit capture and edge topography expression is improved, and data support is provided for sedimentation analysis.

The invention provides a geological envelope body automatic generation method. The method comprises the following steps: defining a target work area; performing sparse horizon picking of a top interface and a bottom interface on the geological envelope in the defined target work area to determine a top interface horizon, a bottom interface horizon and corresponding top interface and bottom interface data; based on the grid positions corresponding to all the pickup point positions of the top interface horizon and the bottom interface horizon, according to the range of the top interface data and the range of the bottom interface data, boundary lines of the geological envelope body at the top interface and the bottom interface in the target work area are determined; determining horizon depth values in all grid points of the top interface and the bottom interface; constructing a top interface boundary constraint and a bottom interface boundary constraint according to the horizon depth value and based on boundary lines of the geological envelope body at the top interface and the bottom interface to determine an intersection point between the top interface and the bottom interface of the envelope body so as to optimize the boundary lines of the geological envelope body at the top interface and the bottom interface; and generating a three-dimensional visualization model of the geological envelope based on the optimized boundary line.

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

The invention relates to the technical field of computer vision, in particular to a sea surface image semantic segmentation method based on boundary feature constraint and electronic equipment, and the method comprises the following steps: S1, obtaining an image; s2, image preprocessing; s3, making a data set; s4, preprocessing the data set; s5, constructing a model; s6, training the model; and S7, outputting a result. According to the double-branch model based on the semantic branches and the boundary branches, expansion of the semantic information in the up-sampling process is restrained by extracting the boundary information and mutual query and constraint of the semantic information, the overall sensing value for a target is constructed in an auxiliary mode, and the problem of dynamic interference is effectively solved; by designing an attention multi-scale sensing module and a bidirectional attention fusion module, the segmentation precision of a water surface small target can be improved, and detail information can be supplemented; by providing the boundary query module and the boundary constraint module, a more accurate target boundary can be obtained on the premise of not introducing excessive parameters.

The invention relates to a vehicle trajectory planning method and device, and a storage medium, and the method comprises the steps: searching a reference line based on a three-lane model, predicting future trajectory points of a vehicle and obstacles around the vehicle based on the reference line, carrying out the time-space discretization of a current position point of the vehicle and a plurality of predicted trajectory points, and carrying out the prediction of the predicted trajectory points; each time layer is bound with a corresponding point, the binding points of any two adjacent time layers are connected to obtain a plurality of connecting edges, and the two adjacent time layers are connected to form a plurality of connecting edges, that is, the vehicle has a plurality of possible driving tracks in a certain time period, and further, the vehicle can be driven by a plurality of driving tracks in a certain time period. And calculating the cost of each connecting edge according to a preset cost function, and performing trajectory planning according to the cost of each connecting edge, a preset constraint condition and the predicted trajectory points of the obstacles bound with the plurality of time layers to obtain a vehicle planning trajectory which meets the lane boundary constraint and has the minimum cost. Therefore, vehicle trajectory planning in a dynamic obstacle avoidance scene is realized.

The invention discloses a multi-lane heterogeneous traffic joint modeling method and system based on a GNN-PINN coupling architecture. The method comprises the following steps: constructing a space-time dynamic graph coding lane changing rule through a GNN, embedding a traffic flow physical constraint through a PINN, and solving a source item; according to the method, CAV behaviors are described by using improved IDM in the mid-microscopic view, an LWR equation subjected to GNN lane change flow correction is solved by using PINN in the macro view, GNN and PINN closed-loop optimization is realized by combining a double-path back propagation mechanism, and a model is optimized based on a residual loss function fusing physical loss, data driving items and boundary constraints. The system comprises a dynamic graph topology module, a GNN coding module, a PINN solving module and the like, the multi-lane traffic dynamic description precision can be improved, the multi-scale characteristic is considered, real-time simulation is supported, and the system is suitable for heterogeneous traffic modeling.

The invention relates to the technical field of power distribution network toughness demand response, in particular to a power distribution network toughness improvement optimization method, system and device based on response prediction and a storage medium. Training an integrated decision tree model based on historical data to predict a response intention value of the cooling and heating load user, and determining a temperature regulation boundary and a response frequency upper limit according to the response intention value; generating an initial scene set through source load uncertainty sampling, extracting a typical scene by adopting a transportation distance scene reduction method, and introducing an overall offset constraint and a single-point extreme value constraint to perform two-dimensional limitation on scene probability distribution; a scene probability combination enabling the load recovery value to be minimum is searched in a probability distribution domain, and a scheduling scheme enabling the key load recovery value to be maximum is solved under the condition that the power flow constraint, the cold and heat power balance constraint and the temperature regulation boundary constraint are met; and carrying out probability weighting on the load recovery values of different scenes to obtain a toughness evaluation value, and carrying out sensitivity analysis.

The invention provides an artificial intelligence-based city scene generation method and a computer readable storage medium, and the method comprises the steps: firstly, carrying out the automatic recognition and extraction of different functional regions based on input graphic data, and carrying out the vectorization of the different functional regions into polygons with structural features; matching a three-dimensional model from a model library according to polygon features and generating an initial layout containing positions and orientations; constructing a differentiable boundary constraint function for each functional region to define a legal region; then defining a differentiable objective function including a boundary loss item and a collision loss item; and finally, by taking the initial layout as a starting point, optimizing an objective function through a gradient descent method, and iteratively adjusting the layout of each three-dimensional model to obtain a final city scene. According to the method, the spatial layout rule is converted into the computable loss function through the differentiable optimization framework, the automatic and intelligent generation of the city scene is realized, the layout generation efficiency is effectively improved, and the layout rationality is ensured.

The invention discloses a water engineering scheduling intelligent parallel caching system for intelligent water conservancy and an optimization method. The system comprises a constraint-driven asynchronous hierarchical caching module, a topology-sensitive cumulative hash module and a polymorphic hierarchical storage module. According to the system, a three-level framework of thread local buffering, global batch processing and an generational sliding window is adopted, and boundary constraint pre-verification is executed on a scheduling scheme; decision vectors are rearranged according to the upstream and downstream topological relation of the water engineering, an upstream engineering decision preferentially participates in Hash calculation and is endowed with a higher weight, and the weight is decreased progressively according to an exponential function; target value arrays are dynamically distributed, and volume class, flow class and time sequence class constraint violation degrees are stored in a layered mode; according to the method, through a topology-aware Hash mechanism and a self-adaptive caching strategy, the recognition precision and the caching hit rate of similar schemes are remarkably improved, repeated calling of a hydraulic simulator is effectively reduced, and the calculation efficiency of water engineering scheduling optimization is improved.

The invention provides an internal micro-grid balance optimization method and system for a source network load storage virtual power plant, and relates to the field of virtual power plants, and the method comprises the steps: obtaining first target power information from a power system, and carrying out the cleaning, standardization, clustering and grading classification processing, and obtaining second target power information; constructing a load model, designing a time sequence network according to the second target power information, and constructing a time sequence model; determining an adjustable variable and an adjustable capability range to obtain a boundary constraint condition; building a double-layer optimization architecture oriented to user requirements and user equipment response scheduling, and constructing a multi-task learning model; and solving the multi-task learning model, and outputting a target solution to adjust the internal micro-grid balance of the virtual power plant. According to the method, the target solution obtained by solving the multi-task learning model can represent the resource allocation instruction required by each device under the optimization target, and then the response strategy is determined to flexibly adjust the user load, so that the multi-target optimization of the source network load storage resources is realized.