44 results about "Model dynamics" patented technology

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a complex scene target detection method based on a model dynamic joint mechanism, and the method comprises the steps: carrying out the preliminary detection through an RT-DETR model, retaining more potential targets through dynamic threshold adjustment, and projecting a generated detection frame to a feature space of an improved YOLOv12 model through dual-mode feature mapping; the improved YOLOv12 model integrates an SEAM attention module and a rejection loss function so as to enhance feature representation and positioning compactness of an occluded target. Then, a model joint mechanism is adopted to process preliminary results of the two models; through difficult case mining and online learning, missing detection targets are supplemented, and the RT-DETR model is optimized; and intelligently fusing the detection results of the two models through dynamic weight distribution based on scene complexity and hierarchical fusion of a decision tree. And finally, post-processing is carried out by using an improved non-maximum suppression algorithm, and mistaken deletion is reduced. According to the method, the problems of missing detection, false detection and inaccurate positioning of the target in a complex scene are effectively solved, and the recall rate and the accuracy rate of detection are remarkably improved.

The invention discloses a tethered satellite system dynamics modeling method considering perturbation factors. The tethered satellite system dynamics modeling method comprises the steps that a geocentric coordinate system, an orbital coordinate system of a Kepler orbit where a tethered satellite system is located and a body coordinate system of a unit satellite are established; according to the movement of the centroid of the tethered satellite system on the orbit, the generalized coordinates are the distance from the centroid of the system to the earth center and the true anomaly of the orbit, the Lagrange function of the centroid of the system is obtained; according to the movement of the unit star relative to the centroid, the generalized coordinates are the distance between the unit star and the centroid, the relative first rotation angle and the relative second rotation angle, the relative Lagrange function of the unit star is obtained; comprehensively considering perturbation factors to obtain non-spherical earth, third gravitational force and solar pressure perturbation potential energy; establishing a system kinetic equation considering comprehensive perturbation factors; and obtaining a complete Lagrange equation of the tethered satellite system. The error problem caused by the fact that the master satellite directly serves as the mass point of the system and the influence of comprehensive perturbation on system motion is not considered is solved, and the number of the slave satellites is not limited.

The invention discloses a three-dimensional model dynamic rendering method and a storage medium, and the method comprises the steps: obtaining three-dimensional data of a target scene, obtaining a plurality of voxels, and enabling each voxel to correspond to a plurality of voxel attributes and an initial comprehensive displacement vector; in any voxel in any time step, obtaining a comprehensive displacement vector of the voxel in the current time step according to a preset physical deformation rule and / or animation curve deformation rule, and performing coordinate transformation on the voxel; after the coordinate transformation, updating the voxel attribute of the voxel corresponding to the current time step length; and performing real-time three-dimensional rendering on the target scene according to the vertex normal of each voxel at the current time step length and the voxel attribute. The physical consistency is ensured in the dynamic deformation process by synthesizing the displacement vector; by updating the voxel attributes, the voxel attributes are kept in smooth transition after deformation; and high-quality real-time rendering of the target scene is realized by recalculating the deformed vertex normal and combining the illumination model.

The invention discloses an automatic driving vehicle dynamics modeling method based on a time-varying parameter neural network, and belongs to the field of vehicle dynamics. Comprising the steps of obtaining operation data of a vehicle under different working conditions, performing preprocessing, and constructing a sample set according to a time sequence slicing mode; constructing a neural network model based on the physical constraints and the time-varying parameters; training and verifying the neural network model by adopting the sample set to obtain a neural network model after parameter optimization for state prediction; the neural network model comprises an input layer, a GRU network, a physical constraint layer and an output layer; input data enters the neural network model through the input layer, and dimension transformation and time sequence packaging are carried out through the linear embedding layer; then time sequence features are extracted through the GRU network, bounded mapping is carried out on output vectors of the GRU network through a physical constraint layer, a time-varying parameter estimation value is obtained, the time-varying parameter estimation value is substituted into a vehicle kinetic equation for calculation, and a physical prediction state is obtained.

The invention discloses an LLMOps-based large model efficient operation and maintenance method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of how to realize large model efficient operation and maintenance, ensure that the large model quickly responds to business requirements in the application process and maintain the performance and stability of the large model. Comprising the steps that an LLMOps tool chain is constructed based on a model deployment tool, a model detection tool, a model upgrading tool, a model running tool and a model iteration tool, and the model deployment tool deploys a large model based on containerization and standardization and CI / CD assembly line automation; the model detection tool is integrated with a verification framework and a real-time monitoring system; the model upgrading tool applies a service network technology to implement gray level upgrading and A / B testing; the model running tool realizes elastic expansion and load balancing of the model service based on an automatic capacity expansion and contraction mechanism of the container arrangement platform; and a model dynamic iteration mechanism based on feedback driving is configured in the model iteration tool.

This invention belongs to the field of ship formation control technology, specifically relating to a neural network-based anti-interference control method for multi-ship formations at sea. It utilizes neural networks to approximate and compensate for uncertainties in the model, while simultaneously constructing a disturbance observer to achieve effective estimation of coupled marine environmental disturbances. To overcome the problem of traditional observers' dependence on accurate models, this method parameterizes the ship motion equations and disturbance terms to establish a canonical model of the disturbance, and uses a disturbance filter to achieve exponential convergence estimation of the disturbance state vector, thus transforming the disturbance suppression problem into an adaptive control problem. Furthermore, this method fully considers input quantization, effectively reducing propeller wear and communication burden by slowing down the rate of change of actuator output. Compared with existing technologies, the anti-interference formation control strategy proposed in this invention can achieve online estimation and suppression of unknown disturbances without relying on any prior model dynamics knowledge.

The invention discloses a financial power transmission right transaction decision optimization method and system based on multi-dimensional dynamic modeling, and relates to the technical field of power market optimization, and the method comprises the steps: collecting multi-source data of a power transmission market, carrying out the preprocessing, extracting a multi-dimensional feature matrix, carrying out the multi-model dynamic collaborative modeling, and generating a joint prediction result; risk index assessment is carried out based on the joint prediction result, and a financial power transmission right transaction decision target expression is generated in combination with constraint conditions by taking revenue maximization and risk minimization as double targets; and solving the financial power transmission right transaction decision target expression by adopting an improved particle swarm optimization algorithm to obtain an optimal financial power transmission right transaction decision scheme. According to the method, the joint prediction result is realized through multi-model dynamic collaborative modeling, the transaction decision target expression is obtained by taking benefit maximization and risk minimization as double targets, and the improved particle swarm optimization is introduced for adaptive optimization, so that the accuracy and robustness of transaction decision can be effectively improved.

This invention provides a method and system for dynamic adaptation and longitudinal-lateral cooperative control of a multi-degree-of-freedom model, comprising: acquiring vehicle driving state parameters and road environment feature parameters; determining the scene category of the current working condition using a scene classifier based on the vehicle driving state parameters and road environment feature parameters; dynamically selecting a matching vehicle dynamics model from a preset multi-degree-of-freedom model library based on the scene category; constructing a prediction model for a model prediction contour controller based on the selected vehicle dynamics model; and optimizing the model prediction contour controller using a multi-objective cost function that includes contour error, path progress, and stability penalty terms to obtain the cooperative longitudinal and lateral control quantities, and performing longitudinal-lateral cooperative control on the vehicle. This invention solves the technical problem that traditional trajectory tracking MPC easily exceeds the stability boundary under extreme working conditions.

The invention relates to a dynamic project cost intelligent prediction method, system and equipment, and the method comprises the steps: anchoring a prediction dimension through a cost motivation map through a closed-loop process of cost motivation quantification, multi-source data structuring, feature engineering and hybrid model dynamic prediction, and carrying out the directional mapping from multi-source data to a cost motivation dimension, performing corresponding feature processing according to feature types on the basis of feature classification defined by the cost motivation quantization atlas to form a structured feature set for model training; project data of different stages are input into a trained mixed cost prediction model, dynamic project cost prediction is achieved, and the mixed cost prediction model comprises an XGBoost model used for extracting static features and an LSTM model based on extracted historical time series data features. Compared with the prior art, the method has the advantages of realizing more accurate and more adaptive project full-stage dynamic cost prediction and the like.

This invention provides an energy-aware intelligent model dynamic pruning and scheduling method and system, relating to the field of model lightweighting technology. By introducing an energy-aware dynamic pruning mechanism, a real-time scheduling strategy, and a closed-loop feedback structure, this invention achieves dynamic optimization of energy consumption, accuracy, and latency during deep learning model inference, offering significant advantages over existing static pruning or fixed model inference schemes. The dynamic pruning decision mechanism of this invention can perform fine-grained structural adjustments based on the real-time energy budget during inference. Its runtime control overhead is far lower than traditional dynamic networks, and it does not significantly increase inference time. The extremely low runtime overhead allows it to operate stably in ultra-low-power scenarios such as wearable devices, IoT nodes, and energy harvesting equipment.

The invention discloses a model reasoning method, a model dynamic encryption method and a model dynamic encryption device, and belongs to the technical field of data processing. The model reasoning method comprises the steps of obtaining a model reasoning request, wherein the model reasoning request is used for requesting a first model to perform model reasoning; obtaining a target model fragment related to the model reasoning request and fragment attribute information of the target model fragment from a trusted execution environment (TEE) storage space, wherein the fragment attribute information is used for indicating at least one of the following items of a calculation layer included in the corresponding model fragment: a layer type, a calculation amount and sensitivity to private data; determining a first model fragment and a second model fragment according to fragment attribute information of the target model fragment and system state information of the electronic equipment; transmitting the second model fragment to a rich execution environment (REE); and based on a scheduler between the TEE and the REE, calling the first model fragment in the TEE, and calling the second model fragment in the REE to execute a processing process corresponding to the model reasoning request.

The invention discloses a mechanical system dynamics modeling method, and relates to the technical field of mechanical dynamics, and the method comprises the steps: decomposing a target mechanical system into a set of a plurality of structural components; performing finite element modeling on each structural component to obtain a finite element model of each structural component; performing dynamic order reduction processing on the finite element model to obtain a polycondensation model of each structural component; integrating and coupling the polycondensation model into a system body dynamic model based on the motion parameters of each motion axis of the target mechanical system and the system assembly relationship; and coupling the dynamical model of the end effector with the system body dynamical model to obtain a target system dynamical model. The whole machine is decomposed into components, order reduction processing is carried out on the components respectively, dynamic integration is carried out according to real-time motion parameters, real dynamic behaviors of a mechanical system in different working spaces are accurately reflected, and limitation of traditional static modeling is overcome.

The application discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic scalable model, which comprises a multi-agent cooperation framework modeling module, a demand understanding and task planning module, a capability verification module, a model dynamic expansion module, a task execution module and a completion module connected in sequence, wherein the multi-agent cooperation framework modeling module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent. The method focuses on a news event semantic understanding scene, constructs a dynamic scalable system architecture through a multi-agent cooperation mechanism, introduces a capability verification link to optimize a task execution process, and realizes on-demand iteration of model capability by using an active learning framework, so that adaptive evolution of agent capability is realized, thereby solving the problems of model rigidity and poor adaptability of a traditional event semantic understanding system in a complex news event scene, and significantly improving the dynamic adaptability and accuracy of news event semantic understanding.

The invention discloses a gas sensor dynamic response modeling method based on a liquid continuous time network, and belongs to the technical field of gas sensors, and the method specifically comprises the following steps: testing mixed gas through a gas sensor array, generating a response curve group, forming a sample point with a concentration sequence group, and intercepting the response sequence group; constructing an initial sample set comprising a concentration sequence group, a response sequence and a real time interval sequence; a continuous time dynamic response neural network model is constructed and trained, and a continuous time closed-form solution neural network unit is specifically used as a core calculation unit. According to the method, the real time interval sequence is used as input, so that the modeling precision and the physical authenticity can be improved. The invention further discloses a self-training data enhancement strategy, an effective way is provided for solving the model training problem under the small sample condition, and the generalization ability and robustness of the model on the aspect of unseen data are improved.

The invention provides a power inspection model dynamic scheduling cooperation method and system based on a hierarchical strategy, and relates to the technical field of power system inspection, and the method comprises the steps: receiving a power inspection task, collecting multi-modal power inspection data, and carrying out the preprocessing; based on a multi-scale convolution feature segmentation algorithm, key features of the electric power inspection task are extracted; evaluating an electric power inspection task level label corresponding to the electric power inspection task in combination with the multi-dimensional task level evaluation index; a corresponding dynamic model scheduling strategy is selected according to the electric power inspection task level label, and models in the dynamic model scheduling strategy comprise a light-weight small model and a cloud large model; and inputting the key features of the electric power inspection task into a model in the dynamic model scheduling strategy, outputting an electric power inspection identification result and pushing the result to the inspection monitoring platform, so that the dynamic model scheduling strategy can be adopted, dynamic distribution and efficient collaboration of model resources are realized, and the real-time performance, the energy efficiency performance and the intelligent level of the electric power inspection system are improved.

The invention discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic extensible model, and the system comprises a multi-agent cooperation framework construction module, a demand understanding and task planning module, a capability verification module, a model dynamic extension module, a task execution module, and a completion module which are connected in sequence. The multi-agent collaboration framework construction module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent, the method focuses on a news event semantic understanding scene, a dynamic extensible system architecture is constructed through a multi-agent collaboration mechanism, a capability verification link is introduced to optimize a task execution process, and the task execution efficiency is improved. And on-demand iteration of the model capability is realized by using an active learning framework, and adaptive evolution of the agent capability is realized, so that the problems of model stiffness, poor adaptability and the like of a traditional event semantic understanding system in a complex news event scene are solved, and the dynamic adaptability and accuracy of news event semantic understanding are remarkably improved.

The application discloses a kind of real scene three-dimensional model dynamic updating system, specifically related to three-dimensional model dynamic updating technical field, and the depth change detection network based on graph attention mechanism obtains candidate change area;Through incremental local reconstruction and preliminary insertion simulation, in combination with two types of high-precision evaluation indexes, namely, redundant reconstruction abnormality coefficient and structure damage pollution coefficient, the reconstruction effect and insertion quality are quantified in real time, and the potential structure continuity damage risk is accurately captured;Secondly, the model updating degradation objective function and degradation index evaluation mechanism are constructed, so that the system can quantitatively monitor the risk of each update behavior, and timely identify the closed-loop trend of "misjudgment-redundant update-structure pollution-again misjudgment";Finally, based on the fluctuation control strategy of updating degradation index, the automatic freezing and pending review marking of high-risk candidate area are realized, and the continuous promotion of safe update path is realized, to ensure the seamless connection of model geometry and texture.

This invention relates to a heave-pitch support mechanism and method for dynamic testing of wind tunnel models, belonging to the field of wind tunnel testing technology. This invention solves the technical problem of distorted measurement results and loss of true reference value caused by the model support mechanism within the wind tunnel. The invention includes a main frame, a slider unit, and a passive buffer device. The passive buffer device is mounted on the main frame, and the slider unit is slidably mounted on the main frame. The heave-pitch support mechanism and method for dynamic testing of wind tunnel models of this invention integrates a magnetic component design in the slider unit, minimizing friction during heave-pitch motion. The single model connecting shaft structure ensures the purity of pitch motion, minimizing the interference of the support system on the model's true dynamic characteristics; thus, it ensures high accuracy and fidelity of the model's motion during wind tunnel testing.

The invention relates to an intelligent lightweight conversion and hierarchical loading method based on model feature recognition, and belongs to the technical field of computer graphic processing and digital twinning. The method comprises the following steps: analyzing an original three-dimensional model and a configuration file thereof, classifying model structure units according to predefined motion constraints, data interfaces and interaction event relationships, and constructing a semantic feature tree; receiving a real-time service data stream, calculating the real-time service criticality of each semantic feature by comparing an entity state with a preset threshold value, dynamically matching a lightweight strategy according to the real-time service criticality, executing a differentiated simplification operation, and generating a lightweight model data set; and calculating a comprehensive loading priority according to the view angle parameter, the user interaction instruction and the real-time business criticality, scheduling and rendering a corresponding feature component according to the comprehensive loading priority, and triggering generation and transmission of a data acquisition frequency regulation and control instruction of the bound data source when rendering detail levels change. Model dynamic optimization and resource closed-loop scheduling of service awareness are realized.

The invention relates to a model dynamic migration decision-making method based on parallel discrete event scheduling. The method comprises the following steps: acquiring multi-dimensional load data of each model in the distributed system; constructing a hybrid prediction model, inputting each piece of multi-dimensional load data into the trained hybrid prediction model, establishing a mapping model of task load and operation time for each simulation event scheduler according to an output load prediction result, dynamically updating parameters of the mapping model by adopting an incremental learning strategy, and obtaining predicted operation time of the simulation event scheduler; constructing a dynamic migration model by taking a scheduler operation time variance in the engine operation process as a target and taking a model unique mounting constraint, a self-migration prohibition constraint and a migration logic coherence constraint as constraint conditions; and solving the dynamic migration model by taking the predicted running time of each simulation event scheduler as a decision basis, and outputting an optimal model migration scheme. By adopting the method, the operation efficiency and the simulation precision of the simulation system can be improved.

The invention discloses a decision-level multi-model dynamic fusion classification method, which comprises the following steps of: establishing each base model, obtaining a data set containing a sample and a real classification category, predicting through the base model to obtain a predicted classification category, calculating a classification accuracy rate in combination with the real category, and constructing a matrix; converting the prediction category into a one-hot coding form to obtain a voting matrix; after filtering the constructed matrix, calculating the contribution weight of each base model by adopting an approximate ideal solution sorting method; and performing scalar multiplication on the voting matrix and the weight to obtain an effective category voting weight matrix, performing Hadamard product on the voting matrix and the weight element by element, performing matrix addition to generate a voting fusion category two-dimensional matrix of all samples, and finally processing the two-dimensional matrix to obtain a fusion classification category. Aiming at the problem that the performance of the existing fusion classification method depends on the sample data quality, more accurate fusion classification is realized by dynamically selecting the dominant basis model and the category which is good at prediction and dynamically endowing the weight.

The invention discloses an adaptive continuous learning time-varying distribution parameter system space-time modeling method, device, equipment and medium, an initial space-time prediction model is constructed through off-line data, after entering an online stage, the space-time non-stability degree of a system is quantified in real time, and the time-varying distribution parameter system space-time modeling method is realized. A space-time forgetting factor is adaptively generated by using a multi-criterion inference mechanism, so that the updating rhythm of the model is dynamically matched with the variable-scale time-varying rhythm of system dynamics, and the problem that a fixed learning rate is difficult to adapt to multi-scale time-varying characteristics is solved; through a space-time collaborative replay learning mechanism, a disastrous forgetting phenomenon in space-time continuous learning is overcome by recall consolidation of a historical core space-time dynamic mode, and the steps of online distributed parameter system data acquisition and model dynamic updating are repeated until an online stage is ended. Accurate tracking and long-term knowledge maintenance of time-varying space-time dynamics are realized, and prediction precision and robustness of a distributed parameter system in a complex non-stationary environment are remarkably improved.

The invention provides a dynamic characteristic modeling method and system for a feeding system based on data physical combined drive, and the method comprises the steps: building a parameterized dynamic model of the feeding system based on the change of the linkage characteristic of a dramatic milling force load and a feeding shaft in a machining track curvature sudden change process; and actual operation state data of the machine tool in the machining track curvature sudden change process are collected, probability modeling and correction are carried out on the prediction error of the parameterized kinetic model based on the actual operation state data, and a corrected kinetic model is obtained. According to the method, through data driving correction, the prediction deviation of a pure physical model under multi-source and random errors is effectively compensated, and the accuracy of predicting the dynamic characteristics of the feeding system under the complex curvature sudden change working condition is remarkably improved.

This invention discloses a dynamic modeling method for a three-orthogonal magnetic reluctance translational magnetic bearing. Based on the equivalent magnetic circuit method, magnetic circuit analysis is performed. Utilizing Ohm's law for magnetic circuits, a single-pole electromagnetic force model of the three-orthogonal magnetic bearing is obtained. Using the lateral edge of a right-angled regular triangular pyramid as a reference, the magnetic bearing's three magnetic poles are evenly distributed circumferentially to construct an orthogonal model magnetic circuit. By analyzing the geometric characteristics of the three-orthogonal magnetic bearing, a coordinate transformation matrix (direction cosine matrix) for the electromagnetic force coordinate system and the position coordinate system is constructed. When the magnetically levitated mover is subjected to external disturbance, the current flowing through the magnetic bearing coil winding is changed according to the change in mover displacement, thereby achieving mover control. The dynamic modeling method for the three-orthogonal magnetic reluctance translational magnetic bearing described in this invention can realize three-degree-of-freedom translation and preset position levitation of the platform on which the magnetic bearing is mounted, and has broad application prospects in the field of attitude control technology for novel spacecraft.

The application discloses a decision-level multi-model dynamic fusion classification method, and steps are as follows: establishing each base model and obtaining a data set containing samples and real classification categories, obtaining a predicted classification category through base model prediction, calculating classification accuracy in combination with the real categories and constructing a matrix; converting the predicted classification category into a one-hot encoding form to obtain a voting matrix; after filtering the constructed matrix, the contribution weight of each base model is calculated by using the TOPSIS method; the voting matrix and the weight are multiplied by a scalar to obtain an effective category voting weight matrix, and after element-wise Hadamard product, a two-dimensional matrix of the voting fusion classification category of all samples is generated through matrix addition, and finally the two-dimensional matrix is processed to obtain the fusion classification category. The application aims at the problem that the performance of the existing fusion classification method depends on the sample data quality, dynamically selects the advantage base model and the category which is good at prediction, and dynamically assigns the weight, so that more accurate fusion classification is realized.

This invention discloses a planning decision-making CIM system supporting dynamic model generation, comprising a display, an operation panel, a core data processing module, and a CIM database. The signal input and output terminals of the core data processing module are connected to the operation panel and the display, respectively, and the communication terminal of the core data processing module is connected to the CIM database. This invention also discloses a planning decision-making method supporting dynamic model generation. This invention features high integration, convenient operation, real-time linkage between indicator input and model dynamic adjustment, and development cost estimation capabilities, and can be widely applied in the field of urban construction informatization.

The invention provides a model dynamic acceleration method and system based on vector retrieval, a medium and equipment. The model dynamic acceleration method based on vector retrieval comprises the following steps: acquiring corpus training data; performing model reasoning based on the corpus training data to obtain an embedded layer output vector and a layer importance sorting list; constructing a hierarchical importance vector knowledge base according to the embedded layer output vector and the layer importance ranking list; processing the hierarchical importance vector knowledge base by adopting a layer skipping strategy based on a query task to obtain a query task result; on the basis of the query task, performing online dynamic retrieval processing in the hierarchical importance vector knowledge base to obtain the layer skipping strategy; and carrying out accelerated decoding processing on the autoregression language model based on the skip layer strategy, and carrying out precision loss optimization on the skip layer by adopting a precision compensation module in the process of accelerated decoding processing. By means of the method, the precision and flexibility of model reasoning acceleration can be improved.

A disclosed method for controlling an aircraft propulsion system includes modeling dynamics of a propulsion system, formulating a problem for achieving the target output as system of linear equations constrained by physical limits of the propulsion system, and generating a solution to the system of linear equations with a singular value decomposition solver module that utilizes Jacobi's singular value decomposition. A control command is generated based on the solution that includes instructions for adjusting a propulsion system operating parameter toward a target output and the propulsion system us operated according to the control command.