157 results about "Boundary constraints" patented technology

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 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 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 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 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 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 provides an intelligent interpretation method for a dual-temporal remote sensing image, which is characterized in that a boundary constraint change detection model BCnet based on a visual basic model is constructed by the method, the universal semantic representation potential of the visual basic model is fully excavated, and a difference detail enhancement module and a multi-scale edge enhancement module are introduced, so that the visual basic model can be fully interpreted. The problem that high-frequency information is lost in the direct migration process of the visual basic model is solved, and fine description of tiny changes and complex textures is achieved. On the basis, an edge feature constraint strategy is designed, an edge feature aggregation module is combined, boundary supervision signals are introduced into a feature space and a prediction space at the same time, and the continuity and geometric integrity of a change detection result in the space structure are enhanced. Experimental results prove that the BCnet has the advantages of high robustness and precision in high-difficulty scenes such as dense building groups and complex edges.

The invention provides a predictive intelligent capacity expansion and contraction method for a large language model inference service and related equipment. The method comprises the following steps: calculating a first instance demand quantity based on a GPU cache utilization rate of a corresponding historical time period in historical monitoring data; obtaining a real-time GPU cache utilization rate of a large language model inference service running in the computing cluster, and calculating according to a preset second target utilization rate to obtain a second instance demand quantity; fusing the first instance demand quantity and the second instance demand quantity to obtain a capacity expansion and contraction prediction instance quantity; boundary constraint is carried out on the capacity expansion and shrinkage prediction instance number so as to be limited between a preset minimum instance number and a preset maximum instance number, and a target instance number is obtained; and executing capacity expansion and contraction operation based on a comparison result of the target instance number and the current running instance number. According to the method, a two-way prediction fusion mechanism of historical monitoring data and real-time monitoring data is introduced, and the response speed and stability of large language model reasoning service are remarkably improved.

The invention discloses a feature-preserved curvature-adaptive triangular mesh generation method. The method comprises the following steps: acquiring a geometric adaptive size field according to a curved surface set based on a geometric size field construction strategy; based on the geometric adaptive size field, performing curve discretization processing on the curve set according to the geometric adaptive size field to obtain a discrete curve grid with reserved curve features; taking the discrete curve grid as a rigid boundary constraint condition, and performing curved surface discretization processing on the parameterized curved surface patches according to a geometric adaptive size field through a leading edge propulsion method and a triangulation coupling strategy to obtain discrete curved surface grids of all the parameterized curved surface patches; and carrying out local grid merging and optimization on the discrete curved surface grid to obtain a uniform curved surface grid with geometric continuity, namely a curvature self-adaptive triangular grid with reserved features. According to the method, the technical problems that the quality of the boundary grid cannot be ensured, the efficiency is low and the convergence is poor in the existing method are solved.

The invention provides a multi-source domain invariant acoustic feature extraction method and system for an equipment operation state, and belongs to the technical field of equipment maintenance, and the method comprises the steps: constructing a multi-source domain invariant acoustic feature extraction network based on a DANN model, and the network comprises a feature extractor, a classifier, a domain discriminator and a multi-domain acoustic feature class boundary constraint module; the feature extractor extracts high-dimensional features of sound signals, the classifier carries out fault mode recognition, and the domain discriminator realizes domain prediction. The multi-domain constraint module generates an embedding space, and calculates the maximum mean value difference, the local maximum mean value difference and the Euclidean distance among different source domain features. The network constructs a loss function by taking minimization of inter-domain difference and classification loss and maximization of inter-domain distance and domain discrimination loss as targets, and carries out adversarial training through multi-source tagged acoustic data. According to the method, domain invariant features are extracted by using adversarial learning and hidden space constraint alignment of multi-source domain acoustic features, so that the influence of feature offset under a cross-working-condition condition is effectively reduced, and the fault mode recognition accuracy is improved.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention discloses an agricultural robot trajectory planning method and system with optimal energy consumption under collision constraint. The method comprises the following steps: acquiring historical state sequence data and environmental map information of a target machine and peripheral machines in real time by a self-machine; identifying the operation intention of the target machine, and outputting a multi-modal prediction trajectory; the method comprises the following steps of: generating a global coarse reference track covering an operation area by taking completion of an own operation task as a main target; generating a space-time safety corridor by adopting a space-time inheritance strategy, and reserving a safety space for the dynamic obstacle; constructing an optimization problem cost function, and comprehensively considering reference trajectory cost, collision risk cost and energy consumption cost; and under the boundary constraint of the safety corridor and the kinematics constraint of the robot, solving the optimization problem by using a numerical optimization solver to obtain an optimized local trajectory. According to the method, safe, efficient and energy-saving collaborative operation can be realized by fusing multi-modal trajectory prediction of the dynamic obstacle, refining energy consumption modeling and risk perception planning.

The invention discloses a territorial space current situation evaluation model method fusing multi-source heterogeneous data, which comprises the following steps of: generating grid units based on topological boundary constraints of an administrative region, and establishing a multi-level index of the grid units of the administrative region through STree; integrating the topological boundary constraint and a point-grid mapping technology to realize that vector space file elements and a table generate a spatial index relationship in the grid unit; according to the grid units and the spatial index relationship, performing label index calculation in the grid by utilizing a labeling calculation tool with natural resource business semantics and fuzzy spatial semantics to obtain a grid label table; and calculating a label index weight based on a CRITIC algorithm fused with the spatial weight matrix, and obtaining a natural resource comprehensive evaluation according to the label index and the label index weight. According to the method, multiple space collaborative evaluation according to the industry field can be supported, and the space calculation scale is effectively unified.

The invention discloses a nonlinear system dynamic boundary constraint particle filtering method, and belongs to the technical field of deep space exploration. The implementation method comprises the following steps: independently sampling all nodes of the flexible lander, and introducing constraints in a particle filtering initial sampling link to enable initial particles to meet the constraints; in the filtering process, as many particles as possible fall within the constraint range in the initial stage of filtering by widening the constraint boundary, and the constraint boundary is continuously shrunk to be gradually close to the real constraint range, so that the situation that a large number of particles fall out of the constraint boundary due to the fact that the real constraint range is too narrow is avoided. According to the method, the constraint boundary is dynamically adjusted, the number of effective particles is increased, and a higher-precision multi-node state estimation result of the flexible lander is obtained; according to the multi-node state of the flexible lander, the particle diversity is ensured through dynamic scaling constraint boundaries, and filtering stop caused by particle degradation is prevented; and realizing real-time trajectory planning and guidance control of the flexible lander by using a node state estimation result.

This invention relates to a multimodal visual understanding method based on consistency learning and hybrid feature extraction. It includes constructing an end-to-end fine-grained consistency learning framework, introducing a hybrid region extractor, fusing local details and global semantics to generate high-quality hybrid visual cue embeddings, combining self-reconstruction loss and latent spatial consistency loss to force the model to establish explicit alignment between the input visual cue and the output segmentation label, utilizing the geometric boundary constraints of the localization task for description generation, and simultaneously optimizing localization accuracy using the semantic depth of the description task. Furthermore, it constructs a detailed localization index expression and segmentation task to enhance the model's reasoning ability for complex long text instructions. The aim is to address the problems of feature fragmentation and insufficient accuracy in existing large models for fine-grained visual localization and description tasks. Compared with existing technologies, this invention has advantages such as high accuracy and strong generalization ability in pixel-level localization and fine-grained description.

A robustness measurement method for LeNet-5 networks based on adversarial boundary constraints is presented, relating to the field of deep learning model testing. The main steps include: for each training sample, dynamically generating adversarial examples based on the model's current state during training iterations; constructing a composite loss function based on standard cross-entropy loss and dynamic boundary constraint loss; performing end-to-end training on all parameters of the LeNet-5 network; and using the overall approximate robustness boundary as a measure of model robustness after training. This method effectively improves the resistance of the LeNet-5 model to fast gradient sign-based adversarial attacks without altering the basic structure of the LeNet-5 network by designing a new loss function.

This invention provides a chassis drive-by-wire method and system based on SAC reinforcement learning and dynamic boundary constraints. The method first collects vehicle dynamics data and preprocesses it to output a standardized dataset. Then, it constructs a multi-dimensional state space vector, fusing scene identifiers to form a feature input system. This is then input into the SAC reinforcement learning agent, and combined with a dynamic weight reward algorithm, it outputs the optimal control decision. Subsequently, the decision is split into two types of control flows and scheduled for output. Finally, the constraint commands are verified, transformed, and issued, and feedback data is collected to form a complete control closed loop. This invention achieves continuous collaborative control and multi-objective optimization of multiple systems by constructing a multi-dimensional feature system, calibrating actuator safety boundaries, and introducing a reinforcement learning framework with entropy regularization, thereby improving decision accuracy and adaptability to all scenarios. Furthermore, through high- and low-frequency control flow splitting and closed-loop feedback, it accurately distributes commands, reduces computational load, continuously optimizes strategies, strengthens safety redundancy, and improves operational stability and reliability.

A kind of open pit model dynamic updating method based on real-time positioning of excavator and SDF belongs to the technical field of digital mine. The initial pit triangular mesh model is voxelized to generate a three-dimensional symbolic distance field based on the definition of vertical coordinate symbol. The three-dimensional positioning data of the center of rotation of the excavator is collected in real time through the GNSS-RTK positioning module, and the working state is judged based on the preset time window and displacement threshold. When the excavator switches from displacement state to digging state, a spherical mining influence domain is constructed with the current positioning data as the center of the sphere and the maximum digging radius as the radius, and the voxel symbol distance field value in the domain is set to 0. A control point set is constructed by boundary constraint points and background constraint points, and global symbol distance field smoothing reconstruction is realized by using radial basis function interpolation. The zero value isosurface is extracted by moving cube algorithm to generate the updated pit model. The present application realizes high-frequency model updating synchronized with operation, has low deployment cost and strong robustness, and solves the problems of model updating lag and visualization penetration.

The application discloses a G1 continuity curved surface reconstruction and generation method based on boundary constraint, and belongs to the technical field of computer aided design and computer graphics technology. Step 1: taking a three-dimensional industrial model to be repaired as input, a unified normalized parameter mapping is established to obtain a standard boundary data set; step 2: taking the standard boundary data set as input, a basic cross-border tangent vector field is obtained; step 3: taking the basic cross-border tangent vector field as input, an original modified cross-border tangent vector field is obtained; step 4: taking the original modified cross-border tangent vector field as input, a tangent plane geometric constraint mechanism is established, and a final cross-border tangent vector field is obtained; step 5: taking the final cross-border tangent vector field as input, a rational harmonic formula is introduced, and four corner point torsion vectors after rational harmonic are obtained; step 6: a high-order B-spline geometric entity meeting an industrial tolerance is generated through a reverse approximation algorithm, and a B-spline curved surface is obtained. The application can automatically generate a high-quality transition curved surface without high-light tearing under complex and disordered boundary constraints.

This application discloses a method and system for constructing a dynamic graph structure based on offline semantic constraint mapping, relating to the fields of semantic information processing and graph structure modeling. The method first constructs a predefined semantic node system to define semantic node identifiers, semantic core descriptions, semantic boundary constraints, and inter-node relationships. In the offline phase, semantic matching is performed between data field descriptions and semantic core descriptions of semantic nodes to generate and solidify an offline semantic mapping table. In the runtime phase, data fields are mapped to corresponding semantic nodes according to the offline semantic mapping table, and standardization and aggregation calculations are performed to obtain node state values. Subsequently, the graph structure is initialized based on node relationships, and edge weights are dynamically calculated and edge activation states are determined based on node states, thereby constructing a dynamic graph structure that responds to the current batch of data. This application achieves consistency and repeatability of semantic mapping by solidifying semantic mapping relationships offline.

The application discloses a grid division method for a closed cylindrical surface with complex boundary constraints, and comprises the following steps: obtaining a plurality of boundary rings of the closed cylindrical surface and a plurality of parameters from a solid model, wherein the parameters comprise a coordinate system of the closed cylindrical surface, a radius of the closed cylindrical surface and an axial direction of the closed cylindrical surface; sequentially performing discrete calculation on each edge in each boundary ring of the closed cylindrical surface to obtain a plurality of discrete points; all the discrete points corresponding to the boundary ring form a discrete point linked list corresponding to the boundary ring; the arrangement order of the discrete points in the discrete point linked list is consistent with the arrangement order of the discrete points in the edge; each discrete point in the discrete point linked list corresponding to each boundary ring is subjected to mapping transformation to obtain a corresponding plane mapping point, and a mapping relationship between the plane mapping point and the corresponding discrete point is established. The application can solve the technical problem that there is no grid division method for a closed cylindrical surface with complex boundary constraints.

The application discloses a dynamic scene SLAM positioning method based on a target detection algorithm YOLOv4 and geometric constraints. Firstly, an image is collected through a visual sensor, a potential moving object area in the image sequence is recognized by using the YOLOv4 target detection algorithm, feature points of a part outside the area are extracted, and the matching feature points are calculated by using an optical flow method. After a transformation matrix of two frames is calculated, whether the feature points are dynamic points is judged by using an epipolar geometry epipolar constraint. When the epipolar constraint is invalid due to degenerate motion, whether the feature points are dynamic feature points is judged by using a boundary constraint. Finally, a dynamic object area is determined in combination with a target detection result, and after the corresponding area is removed, tracking, local mapping and loop detection threads are sequentially performed, a SLAM algorithm for a dynamic scene is realized, and the existing visual SLAM algorithm is inaccurate in pose estimation in a dynamic environment and is prone to be lost. The application significantly improves the visual SLAM pose precision in a dynamic scene while ensuring real-time performance, and can smoothly run on an embedded system.

This invention discloses a method and system for constructing a microscopic model of recycled concrete based on a three-dimensional five-phase spherical model, belonging to the field of numerical simulation technology for building materials. The method includes: constructing a co-simulation environment and creating a new mortar matrix; setting the core particle size of natural aggregate, the thickness of the old interface transition zone, the thickness of the old mortar, and the thickness of the new interface transition zone; generating single-particle aggregates and recursively calculating the outer diameter of each layer; generating random spatial coordinates that satisfy boundary constraints; placing aggregates based on a random sequential adsorption algorithm and Euclidean distance interference judgment criterion; parameterizing the effective aggregates to generate four concentric spherical geometries and marking them according to preset naming rules; repeating the placement until the target volume is reached; and automatically aggregating spherical geometries of the same medium type into an explicit selection set using naming rules. This invention solves the problems of single-dimensionality, missing microphases, and low modeling efficiency in existing technologies, achieving high-precision and automated construction of the five-phase structure of recycled concrete, providing an accurate numerical model for subsequent mechanical property and durability analysis.

The invention belongs to the field of building photovoltaic technology, and particularly relates to a roof column arrangement method based on grid matrix and boundary adaptation, and the method comprises the steps: 1, building a roof geometric model which comprises a main body polygon and an obstacle set; 2, performing gridding discrete processing on the roof area to generate a regular grid array; step 3, performing intelligent obstacle detection and boundary feature recognition on each grid unit, wherein the obstacle detection supports unified processing of common polygonal obstacles S4 and composite structure obstacles; 4, generating an optimal scanning axis based on boundary constraint and an obstacle avoidance strategy, wherein the scanning axis comprises a longitudinal axis and a transverse axis; 5, generating a stand column point location, performing settable test on an axis intersection point, and performing intelligent compensation on a position which does not meet the condition; and 6, executing multi-scheme evaluation and optimization, and automatically selecting an optimal arrangement scheme based on a comprehensive evaluation index.

The invention discloses a power distribution network measurement data restoration method and system considering edge enhancement and multi-scale detail restoration, and relates to the technical field of power system data processing and industrial time series data governance. Real working condition abnormity and dirty data are distinguished, and a repair mask and morphological enhancement prior information are generated; a model is generated through WGAN-GP training with mask consistency and boundary constraint, and a preliminary repair result is output through latent variable inversion; then, repairing details are optimized through edge-oriented form remodeling and multi-scale residual error weighting; and finally, performing back-check iteration by adopting a homologous statistical standard. According to the scheme, the problems of excessive smoothness and edge fault of long block repair are effectively solved, the authenticity and reliability of repair data are improved, and accurate operation and maintenance requirements of power distribution network state monitoring, scheduling optimization and the like can be met.

The invention discloses an SOME / IP service flow-oriented spatio-temporal behavior perception and dynamic shaping method, which comprises the steps of collecting SOME / IP message mirror images on a bypass of a vehicle-mounted network controller main control platform, analyzing and extracting spatio-temporal multi-dimensional features, constructing a service granularity flow sample set based on a sliding window, completing kernel density estimation through self-adaptive gridding discretization and frequency domain accelerated convolution, and obtaining a service granularity flow sample set. Service flow probability density distribution is deeply represented, and time-space communication behaviors of the service flow are quantified through comprehensive information entropy; on-line comparison is decided based on a self-adaptive double-entropy threshold value, and a hierarchical shaping strategy is accurately triggered; and driving the release end ECU to execute in real time through a standard interface, constructing closed-loop recursive feedback by fusing a bottom layer physical state to carry out dynamic correction, and setting a state deviation degree-based severe boundary constraint and deterministic failure rollback mechanism. According to the method, SOME / IP service-level precise management and control, low-delay shaping and vehicle-gauge-level safety guarantee are realized, a bottom-layer protocol stack does not need to be modified, and a data-driven dynamic traffic management normal form is constructed for a vehicle-mounted network.

The invention provides a method and a device for determining a settlement path of a propping agent in a crack. According to the spatial scale parameter and the settlement time parameter, determining a corresponding target crack parameter; determining fluid response parameters of the target crack at different space-time positions by using a first module of a preset proppant settlement prediction model according to the target crack parameters; wherein the proppant settlement prediction model is a neural network model with a composite loss function, and the composite loss function comprises a control equation residual term and a multi-physical boundary constraint term; a second module of a preset proppant sedimentation prediction model is used for determining the sedimentation velocity and position change information of proppant particles in the target crack according to the fluid response parameters; and according to the settlement velocity and the position change information, determining a settlement path of the proppant particles in the target crack in the whole prediction time range. Therefore, the defect that the particle size sedimentation process is difficult to comprehensively reveal in the prior art is overcome.